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1. Exquisitor at the Video Browser Showdown 2024: Relevance Feedback Meets Conversational Search

Author

Khan, Omar Shahbaz, Zhu, Hongyi, Sharma, Ujjwal, Kanoulas, Evangelos, Rudinac, Stevan, Jónsson, Björn Þór, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rudinac, Stevan, editor, Hanjalic, Alan, editor, Liem, Cynthia, editor, Worring, Marcel, editor, Jónsson, Björn Þór, editor, Liu, Bei, editor, and Yamakata, Yoko, editor

Published
2024
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2. Evaluating Performance and Trends in Interactive Video Retrieval: Insights From the 12th VBS Competition

Author

Lucia Vadicamo, Rahel Arnold, Werner Bailer, Fabio Carrara, Cathal Gurrin, Nico Hezel, Xinghan Li, Jakub Lokoc, Sebastian Lubos, Zhixin Ma, Nicola Messina, Thao-Nhu Nguyen, Ladislav Peska, Luca Rossetto, Loris Sauter, Klaus Schoffmann, Florian Spiess, Minh-Triet Tran, and Stefanos Vrochidis

Subjects
Content-based retrieval, interactive evaluation campaign, interactive video retrieval, performance evaluation, video browsing, video content analysis, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This paper conducts a thorough examination of the 12th Video Browser Showdown (VBS) competition, a well-established international benchmarking campaign for interactive video search systems. The annual VBS competition has witnessed a steep rise in the popularity of multimodal embedding-based approaches in interactive video retrieval. Most of the thirteen systems participating in VBS 2023 utilized a CLIP-based cross-modal search model, allowing the specification of free-form text queries to search visual content. This shared emphasis on joint embedding models contributed to balanced performance across various teams. However, the distinguishing factors of the top-performing teams included the adept combination of multiple models and search modes, along with the capabilities of interactive interfaces to facilitate and refine the search process. Our work provides an overview of the state-of-the-art approaches employed by the participating systems and conducts a thorough analysis of their search logs, which record user interactions and results of their queries for each task. Our comprehensive examination of the VBS competition offers assessments of the effectiveness of the retrieval models, browsing efficiency, and user query patterns. Additionally, it provides valuable insights into the evolving landscape of interactive video retrieval and its future challenges.

Published
2024
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3. Interactive video retrieval in the age of effective joint embedding deep models: lessons from the 11th VBS.

Author

Lokoč, Jakub, Andreadis, Stelios, Bailer, Werner, Duane, Aaron, Gurrin, Cathal, Ma, Zhixin, Messina, Nicola, Nguyen, Thao-Nhu, Peška, Ladislav, Rossetto, Luca, Sauter, Loris, Schall, Konstantin, Schoeffmann, Klaus, Khan, Omar Shahbaz, Spiess, Florian, Vadicamo, Lucia, and Vrochidis, Stefanos

Subjects
*INTERACTIVE videos, *TASK performance
Abstract

This paper presents findings of the eleventh Video Browser Showdown competition, where sixteen teams competed in known-item and ad-hoc search tasks. Many of the teams utilized state-of-the-art video retrieval approaches that demonstrated high effectiveness in challenging search scenarios. In this paper, a broad survey of all utilized approaches is presented in connection with an analysis of the performance of participating teams. Specifically, both high-level performance indicators are presented with overall statistics as well as in-depth analysis of the performance of selected tools implementing result set logging. The analysis reveals evidence that the CLIP model represents a versatile tool for cross-modal video retrieval when combined with interactive search capabilities. Furthermore, the analysis investigates the effect of different users and text query properties on the performance in search tasks. Last but not least, lessons learned from search task preparation are presented, and a new direction for ad-hoc search based tasks at Video Browser Showdown is introduced. [ABSTRACT FROM AUTHOR]

Published
2023
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4. Exquisitor at the Video Browser Showdown 2022

Author

Khan, Omar Shahbaz, Sharma, Ujjwal, Jónsson, Björn Þór, Koelma, Dennis C., Rudinac, Stevan, Worring, Marcel, Zahálka, Jan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Þór Jónsson, Björn, editor, Gurrin, Cathal, editor, Tran, Minh-Triet, editor, Dang-Nguyen, Duc-Tien, editor, Hu, Anita Min-Chun, editor, Huynh Thi Thanh, Binh, editor, and Huet, Benoit, editor

Published
2022
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6. Leveraging CD Gain for Precise Barehand Video Timeline Browsing on Smart Displays

Author

Zhang, Futian, Mizobuchi, Sachi, Zhou, Wei, Khan, Taslim Arefin, Li, Wei, Lank, Edward, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ardito, Carmelo, editor, Lanzilotti, Rosa, editor, Malizia, Alessio, editor, Petrie, Helen, editor, Piccinno, Antonio, editor, Desolda, Giuseppe, editor, and Inkpen, Kori, editor

Published
2021
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7. Exquisitor at the Video Browser Showdown 2021: Relationships Between Semantic Classifiers

Author

Khan, Omar Shahbaz, Jónsson, Björn Þór, Larsen, Mathias, Poulsen, Liam, Koelma, Dennis C., Rudinac, Stevan, Worring, Marcel, Zahálka, Jan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lokoč, Jakub, editor, Skopal, Tomáš, editor, Schoeffmann, Klaus, editor, Mezaris, Vasileios, editor, Li, Xirong, editor, Vrochidis, Stefanos, editor, and Patras, Ioannis, editor

Published
2021
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8. The MovieWall: A New Interface for Browsing Large Video Collections

Author

Nefkens, Marij, Hürst, Wolfgang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lokoč, Jakub, editor, Skopal, Tomáš, editor, Schoeffmann, Klaus, editor, Mezaris, Vasileios, editor, Li, Xirong, editor, Vrochidis, Stefanos, editor, and Patras, Ioannis, editor

Published
2021
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9. Exquisitor at the Video Browser Showdown 2020

Author

Jónsson, Björn Þór, Khan, Omar Shahbaz, Koelma, Dennis C., Rudinac, Stevan, Worring, Marcel, Zahálka, Jan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ro, Yong Man, editor, Cheng, Wen-Huang, editor, Kim, Junmo, editor, Chu, Wei-Ta, editor, Cui, Peng, editor, Choi, Jung-Woo, editor, Hu, Min-Chun, editor, and De Neve, Wesley, editor

Published
2020
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10. Video Browsing on a Circular Timeline

Author

Münzer, Bernd, Schoeffmann, Klaus, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Schoeffmann, Klaus, editor, Chalidabhongse, Thanarat H., editor, Ngo, Chong Wah, editor, Aramvith, Supavadee, editor, O’Connor, Noel E., editor, Ho, Yo-Sung, editor, Gabbouj, Moncef, editor, and Elgammal, Ahmed, editor

Published
2018
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11. A Video Library System Using Scene Detection and Automatic Tagging

Author

Baraldi, Lorenzo, Grana, Costantino, Cucchiara, Rita, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Grana, Costantino, editor, and Baraldi, Lorenzo, editor

Published
2017
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12. Vetrina Attori: Scene Seek Support System Focusing on Characters in a Video

Author

Narahara, Masahiro, Matsumura, Kohei, Lopez-Gulliver, Roberto, Noma, Haruo, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Munekata, Nagisa, editor, Kunita, Itsuki, editor, and Hoshino, Junichi, editor

Published
2017
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13. Storyboard-Based Video Browsing Using Color and Concept Indices

Author

Hürst, Wolfgang, Ching, Algernon Ip Vai, Schoeffmann, Klaus, Primus, Manfred J., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Amsaleg, Laurent, editor, Guðmundsson, Gylfi Þór, editor, Gurrin, Cathal, editor, Jónsson, Björn Þór, editor, and Satoh, Shin’ichi, editor

Published
2017
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14. An Evaluation of Video Browsing on Tablets with the ThumbBrowser

Author

Hudelist, Marco A., Schoeffmann, Klaus, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Amsaleg, Laurent, editor, Guðmundsson, Gylfi Þór, editor, Gurrin, Cathal, editor, Jónsson, Björn Þór, editor, and Satoh, Shin’ichi, editor

Published
2017
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15. Mental Visual Browsing

Author

He, Jun, Shang, Xindi, Zhang, Hanwang, Chua, Tat-Seng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tian, Qi, editor, Sebe, Nicu, editor, Qi, Guo-Jun, editor, Huet, Benoit, editor, Hong, Richang, editor, and Liu, Xueliang, editor

Published
2016
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16. Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown

Author

Heller, Silvan, Gsteiger, Viktor, Bailer, Werner, Gurrin, Cathal, Jónsson, Björn Þór, Lokoč, Jakub, Leibetseder, Andreas, Mejzlík, František, Peška, Ladislav, Rossetto, Luca, Schall, Konstantin, Schoeffmann, Klaus, Schuldt, Heiko, Spiess, Florian, Tran, Ly-Duyen, Vadicamo, Lucia, Veselý, Patrik, Vrochidis, Stefanos, and Wu, Jiaxin

Published
2022
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17. Wireless Video Surveillance System Based on Incremental Learning Face Detection

Author

Liao, Wenjuan, Zeng, Dingheng, Zhou, Liguo, Wang, Shizheng, Zhong, Huicai, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, He, Xiangjian, editor, Luo, Suhuai, editor, Tao, Dacheng, editor, Xu, Changsheng, editor, Yang, Jie, editor, and Hasan, Muhammad Abul, editor

Published
2015
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18. A Surveillance Video Index and Browsing System Based on Object Flags and Video Synopsis

Author

Ye, Gensheng, Liao, Wenjuan, Dong, Jichao, Zeng, Dingheng, Zhong, Huicai, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, He, Xiangjian, editor, Luo, Suhuai, editor, Tao, Dacheng, editor, Xu, Changsheng, editor, Yang, Jie, editor, and Hasan, Muhammad Abul, editor

Published
2015
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19. Tell Me about TV Commercials of This Product

Author

Zhu, Cai-Zhi, Kasamwattanarote, Siriwat, Wu, Xiaomeng, Satoh, Shin’ichi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gurrin, Cathal, editor, Hopfgartner, Frank, editor, Hurst, Wolfgang, editor, Johansen, Håvard, editor, Lee, Hyowon, editor, and O’Connor, Noel, editor

Published
2014
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20. How Do Users Search with Basic HTML5 Video Players?

Author

Cobârzan, Claudiu, Schoeffmann, Klaus, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gurrin, Cathal, editor, Hopfgartner, Frank, editor, Hurst, Wolfgang, editor, Johansen, Håvard, editor, Lee, Hyowon, editor, and O’Connor, Noel, editor

Published
2014
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22. Exploration de l’activité de publication et de recherche de vidéos sur une plateforme audiovisuelle académique en ligne

Author

Emmanuelle Papinot, André Tricot, and Mônica Macedo-Rouet

Subjects
information seeking, video browsing, video retrieval, audiovisual publication, academic, Psychology, BF1-990, Social Sciences
Abstract

Publishing and searching for videos online are two common human activities that remain under-investigated today. We investigated these two related activities through interviews with users of an online academic audiovisual database. Seven publication managers and eight users were asked to explain their goals and strategies for publishing/searching for videos in the database. The results show that the way publication managers represent the publication task, in its technical, editorial, documentary and knowledge organization aspects, has an impact on the users’ searching task, and more specifically on how they understand the collection and organization of knowledge, the location and identification of a resource, as well as the services supporting video viewing. These results suggest that different categories of consultation goals and modus operandi exist. The discussion examines the aspects that should be considered in the ergonomic design of this type of academic audiovisual database.

Published
2018
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23. Use of Content Analysis Tools for Visual Interaction Design

Author

Mohamad Ali, Nazlena, Lee, Hyowon, Smeaton, Alan F., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Zaman, Halimah Badioze, editor, Robinson, Peter, editor, Petrou, Maria, editor, Olivier, Patrick, editor, Shih, Timothy K., editor, Velastin, Sergio, editor, and Nyström, Ingela, editor

Published
2011
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24. On Influential Trends in Interactive Video Retrieval: Video Browser Showdown 2015–2017.

Author

Lokoc, Jakub, Bailer, Werner, Schoeffmann, Klaus, Muenzer, Bernd, and Awad, George

Abstract

The last decade has seen innovations that make video recording, manipulation, storage, and sharing easier than ever before, thus impacting many areas of life. New video retrieval scenarios emerged as well, which challenge the state-of-the-art video retrieval approaches. Despite recent advances in content analysis, video retrieval can still benefit from involving the human user in the loop. We present our experience with a class of interactive video retrieval scenarios and our methodology to stimulate the evolution of new interactive video retrieval approaches. More specifically, the video browser showdown evaluation campaign is thoroughly analyzed, focusing on the years 2015–2017. Evaluation scenarios, objectives, and metrics are presented, complemented by the results of the annual evaluations. The results reveal promising interactive video retrieval techniques adopted by the most successful tools and confirm assumptions about the different complexity of various types of interactive retrieval scenarios. A comparison of the interactive retrieval tools with automatic approaches (including fully automatic and manual query formulation) participating in the TRECVID 2016 ad hoc video search task is discussed. Finally, based on the results of data analysis, a substantial revision of the evaluation methodology for the following years of the video browser showdown is provided. [ABSTRACT FROM AUTHOR]

Published
2018
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25. Exploration de l'activité de publication et de recherche de vidéos sur une plateforme audiovisuelle académique en ligne.

Author

Papinot, Emmanuelle, Tricot, André, and Macedo-Rouet, Mônica

Abstract

Publishing and searching for videos online are two common human activities that remain underinvestigated today. We investigated these two related activities through interviews with users of an online academic audiovisual database. Seven publication managers and eight users were asked to explain their goals and strategies for publishing/searching for videos in the database. The results show that the way publication managers represent the publication task, in its technical, editorial, documentary and knowledge organization aspects, has an impact on the users' searching task, and more specifically on how they understand the collection and organization of knowledge, the location and identification of a resource, as well as the services supporting video viewing. These results suggest that different categories of consultation goals and modus operandi exist. The discussion examines the aspects that should be considered in the ergonomic design of this type of academic audiovisual database. [ABSTRACT FROM AUTHOR]

Published
2018
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26. Are Visual Informatics Actually Useful in Practice: A Study in a Film Studies Context

Author

Mohamad Ali, Nazlena, Smeaton, Alan F., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Badioze Zaman, Halimah, editor, Robinson, Peter, editor, Petrou, Maria, editor, Olivier, Patrick, editor, Schröder, Heiko, editor, and Shih, Timothy K., editor

Published
2009
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27. Developing, Deploying and Assessing Usage of a Movie Archive System among Students of Film Studies

Author

Mohamad Ali, Nazlena, Smeaton, Alan F., Lee, Hyowon, Brereton, Pat, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Jacko, Julie A., editor

Published
2009
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29. Object Re-detection Using SIFT and MPEG-7 Color Descriptors

Author

Schügerl, Philipp, Sorschag, Robert, Bailer, Werner, Thallinger, Georg, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sebe, Nicu, editor, Liu, Yuncai, editor, Zhuang, Yueting, editor, and Huang, Thomas S., editor

Published
2007
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30. Video Streaming to Mobile Handheld Devices: Challenges in Decoding, Adaptation, and Browsing

Author

Chen, Chang Wen, Li, Houqiang, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sebe, Nicu, editor, Liu, Yuncai, editor, Zhuang, Yueting, editor, and Huang, Thomas S., editor

Published
2007
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31. Sharing Video Browsing Style by Associating Browsing Behavior with Low-Level Features of Videos

Author

Takashima, Akio, Tanaka, Yuzuru, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Jacko, Julie A., editor

Published
2007
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32. Attention Information Based Spatial Adaptation Framework for Browsing Videos Via Mobile Devices

Author

Wang, Yi, Li, Houqiang, Liu, Zhengkai, Chen, Chang Wen, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhuang, Yueting, editor, Yang, Shi-Qiang, editor, Rui, Yong, editor, and He, Qinming, editor

Published
2006
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33. Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019

Author

Andreas Leibetseder, Stefanos Vrochidis, Klaus Schoeffmann, Luca Rossetto, Tomáš Souček, Werner Bailer, Bernd Muenzer, Paolo Bolettieri, Phuong Anh Nguyen, Ralph Gasser, Jakub Lokoč, and University of Zurich

Subjects
Information retrieval, Artificial neural network, 10009 Department of Informatics, Computer science, business.industry, Interactive video, Deep learning, Video content analysis, 02 engineering and technology, 000 Computer science, knowledge & systems, Computer Science Applications, Visualization, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, Task analysis, 020201 artificial intelligence & image processing, Video browsing, Relevance (information retrieval), Artificial intelligence, Electrical and Electronic Engineering, business
Abstract

Despite the fact that automatic content analysis has made remarkable progress over the last decade - mainly due to significant advances in machine learning - interactive video retrieval is still a very challenging problem, with an increasing relevance in practical applications. The Video Browser Showdown (VBS) is an annual evaluation competition that pushes the limits of interactive video retrieval with state-of-the-art tools, tasks, data, and evaluation metrics. In this paper, we analyse the results and outcome of the 8th iteration of the VBS in detail. We first give an overview of the novel and considerably larger V3C1 dataset and the tasks that were performed during VBS 2019. We then go on to describe the search systems of the six international teams in terms of features and performance. And finally, we perform an in-depth analysis of the per-team success ratio and relate this to the search strategies that were applied, the most popular features, and problems that were experienced. A large part of this analysis was conducted based on logs that were collected during the competition itself. This analysis gives further insights into the typical search behavior and differences between expert and novice users. Our evaluation shows that textual search and content browsing are the most important aspects in terms of logged user interactions. Furthermore, we observe a trend towards deep learning based features, especially in the form of labels generated by artificial neural networks. But nevertheless, for some tasks, very specific content-based search features are still being used. We expect these findings to contribute to future improvements of interactive video search systems.

Published
2021
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34. Interactive video search tools: a detailed analysis of the video browser showdown 2015.

Author

Cobârzan, Claudiu, Schoeffmann, Klaus, Bailer, Werner, Hürst, Wolfgang, Blažek, Adam, Lokoč, Jakub, Vrochidis, Stefanos, Barthel, Kai, and Rossetto, Luca

Subjects
MULTIMEDIA systems, INFORMATION storage & retrieval systems, INTERACTIVE videos, WEB browsing, CONFERENCES & conventions
Abstract

Interactive video retrieval tools developed over the past few years are emerging as powerful alternatives to automatic retrieval approaches by giving the user more control as well as more responsibilities. Current research tries to identify the best combinations of image, audio and text features that combined with innovative UI design maximize the tools performance. We present the last installment of the Video Browser Showdown 2015 which was held in conjunction with the International Conference on MultiMedia Modeling 2015 (MMM 2015) and has the stated aim of pushing for a better integration of the user into the search process. The setup of the competition including the used dataset and the presented tasks as well as the participating tools will be introduced . The performance of those tools will be thoroughly presented and analyzed. Interesting highlights will be marked and some predictions regarding the research focus within the field for the near future will be made. [ABSTRACT FROM AUTHOR]

Published
2017
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35. Video summarization via block sparse dictionary selection

Author

Zhiyong Wang, Shaohui Mei, Junhui Hou, David Dagan Feng, Mingyang Ma, and Shuai Wan

Subjects
0209 industrial biotechnology, Computer science, business.industry, Cognitive Neuroscience, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, 02 engineering and technology, Video processing, Sparse approximation, Matching pursuit, Automatic summarization, Computer Science Applications, 020901 industrial engineering & automation, Artificial Intelligence, Robustness (computer science), Outlier, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Video browsing, Artificial intelligence, business
Abstract

The explosive growth of video data has raised new challenges for many video processing tasks such as video browsing and retrieval, hence, effective and efficient video summarization (VS) is urgently demanded to automatically summarize a video into a succinct version. Recent years have witnessed the advancements of sparse representation based approaches for VS. However, video frames are analyzed individually for keyframe selection in existing methods, which could lead to redundancy among selected keyframes and poor robustness to outlier frames. Due to that adjacent frames are visually similar, candidate keyframes often occur in temporal blocks, in addition to sparse presence. Therefore, in this paper, the block-sparsity of candidate keyframes is taken into consideration, by which the VS problem is formulated as a block sparse dictionary selection model. Moreover, a simultaneous block version of Orthogonal Matching Pursuit (SBOMP) algorithm is designed for model optimization. Two keyframe selection strategies are also explored for each block. Experimental results on two benchmark datasets, namely VSumm and TVSum datasets, demonstrate that the proposed SBOMP based VS method clearly outperforms several state-of-the-art sparse representation based methods in terms of F-score, redundancy among keyframes and robustness to outlier frames.

Published
2020
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36. Video Synopsis Based on Attention Mechanism and Local Transparent Processing

Author

Huaikou Miao, Xianrui Liu, Congcong Zhou, Shengbo Chen, and Yiyong Huang

Subjects
General Computer Science, Computer science, business.industry, Search engine indexing, Feature extraction, General Engineering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, transparency processing, deep learning, 020206 networking & telecommunications, 02 engineering and technology, Object (computer science), Object detection, Reduction (complexity), 0202 electrical engineering, electronic engineering, information engineering, Video synopsis, 020201 artificial intelligence & image processing, General Materials Science, Computer vision, Video browsing, Artificial intelligence, lcsh:Electrical engineering. Electronics. Nuclear engineering, business, attention mechanism, lcsh:TK1-9971
Abstract

The increased number of video cameras makes an explosive growth in the amount of captured video, especially the increase of millions of surveillance cameras that operate 24 hours a day. Since video browsing and retrieval is time consuming, while video synopsis is one of the most effective ways for browsing and indexing such video that enables the review of hours of video in just minutes. How to generate the video synopsis and preserve the essential activities in the original video is still a costly and labor-intensive and time-intensive work. This paper proposes an approach to generating video synopsis with complete foreground and clearer trajectory of moving objects. Firstly, the one-stage CNN-based object detecting has been employed in object extraction and classification. Then, combining integrating the attention-RetinaNet with Local Transparency-Handling Collision (LTHC) algorithm is given out which results in the trajectory combination optimization and makes the trajectory of the moving object more clearly. Finally, the experiments show that the useful video information is fully retained in the result video, the detection accuracy is improved by 4.87% and the compression ratio reaches 4.94, but the reduction of detection time is not obvious.

Published
2020

37. Recurrent Compressed Convolutional Networks for Short Video Event Detection

Author

Ping Li and Xianghua Xu

Subjects
General Computer Science, business.industry, Event (computing), Computer science, Speech recognition, General Engineering, temporal dependency, Compressed domain, Discriminative model, Benchmark (computing), recurrent neural networks, General Materials Science, Video browsing, The Internet, lcsh:Electrical engineering. Electronics. Nuclear engineering, event analysis, Set (psychology), business, lcsh:TK1-9971, Feature learning, short video event detection, Decoding methods
Abstract

Short videos are popular information carriers on the Internet, and detecting events from them can well benefit widespread applications, e.g., video browsing, management, retrieval and recommendation. Existing video analysis methods always require decoding all frames of videos in advance, which is very costly in time and computation power. These short videos are often untrimmed, noisy and even incomplete, adding much difficulty to event analysis. Unlike previous works focusing on actions, we target short video event detection and propose Recurrent Compressed Convolutional Networks (RCCN) for discovering the underlying event patterns within short videos possibly including a large proportion of non-event videos. Instead of using the whole videos, RCCN performs representation learning at much lower cost within the compressed domain where the encoded motion information reflecting the spatial relations among frames can be easily obtained to capture dynamic tendency of event videos. This alleviates the information incompleteness problem that frequently emerges in user-generated short videos. In particular, RCCN leverages convolutional networks as the backbone and the Long Short-Term Memory components to model the variable-range temporal dependency among untrimmed video frames. RCCN not only learns the common representation shared by the short videos of the same event, but also obtains the discriminative ability to detect dissimilar videos. We benchmark the model performance on a set of short videos generated from publicly available event detection database YLIMED, and compare RCCN with several baselines and state-of-the-art alternatives. Empirical studies have verified the preferable performance of RCCN.

Published
2020
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38. Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown

Author

Silvan Heller, Viktor Gsteiger, Werner Bailer, Cathal Gurrin, Björn Þór Jónsson, Jakub Lokoč, Andreas Leibetseder, František Mejzlík, Ladislav Peška, Luca Rossetto, Konstantin Schall, Klaus Schoeffmann, Heiko Schuldt, Florian Spiess, Ly-Duyen Tran, Lucia Vadicamo, Patrik Veselý, Stefanos Vrochidis, Jiaxin Wu, and University of Zurich

Subjects
Interactive video retrieval, 10009 Department of Informatics, 11476 Digital Society Initiative, Video content analysis, Regular Paper, Media Technology, Video browsing, Content-based retrieval, Evaluations, 000 Computer science, knowledge & systems, Library and Information Sciences, Information Systems
Abstract

The Video Browser Showdown addresses difficult video search challenges through an annual interactive evaluation campaign attracting research teams focusing on interactive video retrieval. The campaign aims to provide insights into the performance of participating interactive video retrieval systems, tested by selected search tasks on large video collections. For the first time in its ten year history, the Video Browser Showdown 2021 was organized in a fully remote setting and hosted a record number of sixteen scoring systems. In this paper, we describe the competition setting, tasks and results, and give an overview of state-of-the-art methods used by the competing systems. By looking at query result logs provided by ten systems, we analyze differences in retrieval model performances and browsing times before a correct submission. Through advances in data gathering methodology and tools, we provide a comprehensive analysis of ad-hoc video search tasks, discuss results, task design and methodological challenges. We highlight that almost all top performing systems utilize some sort of joint embedding for text-image retrieval and enable specification of temporal context in queries for known-item search. Whereas a combination of these techniques drive the currently top performing systems, we identify several future challenges for interactive video search engines and the Video Browser Showdown competition itself.

Published
2022
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41. Scalable storyboards in handheld devices: applications and evaluation metrics.

Author

Herranz, Luis and Jiang, Shuqiang

Subjects
POCKET computers, INFORMATION retrieval, WEB browsing, STREAMING video & television, STORYBOARDS, CONTENT analysis
Abstract

Summaries are an essential component of video retrieval and browsing systems. Most research in video summarization has focused on content analysis to obtain compact yet comprehensive representations of video items. However, important aspects such as how they can be effectively integrated in mobile interfaces and how to predict the quality and usability of the summaries have not been investigated. Conventional summaries are limited to a single instance with certain length (i.e. a single scale). In contrast, scalable summaries target representations with multiple scales, that is, a set of summaries with increasing length in which longer summaries include more information about the video. Thus, scalability provides high flexibility that can be exploited in devices such as smartphones or tablets to provide versions of the summary adapted to the limited visualization area. In this paper, we explore the application of scalable storyboards to summary adaptation and zoomable video navigation in handheld devices. By introducing a new adaptation dimension related with the summarization scale, we can formulate navigation and adaptation in a two-dimensional adaptation space, where different navigation actions modify the trajectory in that space. We also describe the challenges to evaluate scalable summaries and some usability issues that arise from having multiple scales, proposing some objective metrics that can provide useful insight about their potential quality and usability without requiring very costly user studies. Experimental results show a reasonable agreement with the trends shown in subjective evaluations. Experiments also show that content-based scalable storyboards are less redundant and useful than the content-blind baselines. [ABSTRACT FROM AUTHOR]

Published
2016
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42. A Novel Method on Summarization of Video Using Local Ternary Pattern and Local Phase Quantization

Author

Sasmita Kumari Nayak and Jharna Majumdhar

Subjects
business.industry, Computer science, Texture Descriptor, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Automatic summarization, Object detection, Field (computer science), Affinity propagation, Unsupervised learning, Video browsing, Artificial intelligence, Cluster analysis, business
Abstract

In last decade, Video Summarization (VS) approach is playing a pivotal role in the analysis of the Video contents. The methodologies involved in Video Summarization have wide range of applications in the field of defense for video surveillance, intrusion, object detection, Video Browsing, Content-based Video Retrieval and Storage etc. In this study, we have proposed video summarization techniques to extract the frames of interest. Then, video summarization has determined by the advanced texture descriptors. Local Ternary Pattern (LTP) & Local Phase Quantization (LPQ) are the texture descriptor methods used to provide an efficient video summarization process. These methodologies are in conformity with the elimination of redundant frames in a video as well as the maintenance of user defined number of distinctive images. Then apply the clustering process, which is an unsupervised machine learning algorithms, such as, Affinity Propagation and BIRCH, are utilized to cluster the similar frames into one group. These methodologies confirm that the summary of video denotes the most distinctive frames of the input video, which results the same importance to preserve the continuousness of the summarized video.

Published
2021
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43. Video synopsis: A survey

Author

Refik Samet and Kemal Batuhan Baskurt

Subjects
Information retrieval, Computer science, Data domain, Frame (networking), 020207 software engineering, Subject (documents), 02 engineering and technology, Automatic summarization, Field (computer science), Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Video browsing, Computer Vision and Pattern Recognition, Cluster analysis, Representation (mathematics), Software
Abstract

Video synopsis is an activity-based video condensation approach to achieve efficient video browsing and retrieval for surveillance cameras. It is one of the most effective ways to reduce the inactive density of input video in order to provide fast and easy retrieval of the parts of interest. Unlike frame-based video summarization methods, the interested activities are shifted in the time domain to obtain video representation that is more compact. Although the number of studies on video synopsis has increased over the past years, there has still been no survey study on the subject. The aim in this article is to review state-of-the-art approaches in video synopsis studies and provide a comprehensive analysis. The methodology of video synopsis is described to provide an overview on the flow of the algorithm. Recent literature is investigated into different aspects such as optimization type, camera topology, input data domain, and activity clustering mechanisms. Commonly used performance evaluation techniques are also examined. Finally, the current situation of the literature and potential future research directions are discussed after an exhaustive analysis that covers most of the studies from early on to the present in this field. To the best of our knowledge, this study is the first review of published video synopsis approaches.

Published
2019
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44. Efficient Bronchoscopic Video Summarization

Author

William E. Higgins and Patrick D. Byrnes

Subjects
Motion analysis, Lung Neoplasms, Computer science, Shot (filmmaking), 0206 medical engineering, Video Recording, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Biomedical Engineering, 02 engineering and technology, Article, Set (abstract data type), Imaging, Three-Dimensional, Bronchoscopy, Image Processing, Computer-Assisted, Humans, Segmentation, Computer vision, Lung, Phantoms, Imaging, business.industry, 020601 biomedical engineering, Automatic summarization, Video browsing, Artificial intelligence, Tomography, X-Ray Computed, business, Algorithms
Abstract

Bronchoscopy enables many minimally invasive chest procedures for diseases such as lung cancer and asthma. Guided by the bronchoscope's video stream, a physician can navigate the complex three-dimensional (3-D) airway tree to collect tissue samples or administer a disease treatment. Unfortunately, physicians currently discard procedural video because of the overwhelming amount of data generated. Hence, they must rely on memory and anecdotal snapshots to document a procedure. We propose a robust automatic method for summarizing an endobronchial video stream. Inspired by the multimedia concept of the video summary and by research in other endoscopy domains, our method consists of three main steps: 1) shot segmentation, 2) motion analysis, and 3) keyframe selection. Overall, the method derives a true hierarchical decomposition, consisting of a shot set and constituent keyframe set, for a given procedural video. No other method to our knowledge gives such a structured summary for the raw, unscripted, unedited videos arising in endoscopy. Results show that our method more efficiently covers the observed endobronchial regions than other keyframe-selection approaches and is robust to parameter variations. Over a wide range of video sequences, our method required on average only 6.5% of available video frames to achieve a video coverage = 92.7%. We also demonstrate how the derived video summary facilitates direct fusion with a patient's 3-D chest computed-tomography scan in a system under development, thereby enabling efficient video browsing and retrieval through the complex airway tree.

Published
2019
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45. Post-surgical Endometriosis Segmentation in Laparoscopic Videos

Author

Klaus Schoeffmann, Andreas Leibetseder, Jörg Keckstein, and Simon Keckstein

Subjects
Laparoscopic surgery, medicine.medical_specialty, Post surgical, Lesion segmentation, Computer science, General surgery, medicine.medical_treatment, Endometriosis, Image segmentation, Visual appearance, medicine.disease, medicine, Video browsing, Segmentation
Abstract

Endometriosis is a common women’s condition exhibiting a manifold visual appearance in various body-internal locations. Having such properties makes its identification very difficult and error-prone, at least for laymen and non-specialized medical practitioners. In an attempt to provide assistance to gynecologic physicians treating endometriosis, this demo paper describes a system that is trained to segment one frequently occurring visual appearance of endometriosis, namely dark endometrial implants. The system is capable of analyzing laparoscopic surgery videos, annotating identified implant regions with multi-colored overlays and displaying a detection summary for improved video browsing.

Published
2021
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46. Video Retrieval Using Automatically Extracted Audio.

Author

Kale, Anil and Wakde, D.G.

Abstract

Videos are a powerful and expressive media that can capture and present information. As these videos cover many subjects / genres, it is critical task to segregate videos as per the needs or interest of the users which requires classifications. In this paper we present a model for video classification using embedded audio. Video classification is usually accompanied with video annotation which helps in retrieving the archives. Efficient and effective video classification and annotation demands automated unsupervised classification and annotation of videos based on its embedded video content as manual indexing is unfeasible. As a first step the audio content of video is extracted and cleaned for further processing the next step converts audio into textual format. The text is processed upon to get the prime keywords in the video using text mining. The videos are classified and annotated on the keywords thus found. The annotated videos are stored in an object oriented database for future retrieval. [ABSTRACT FROM PUBLISHER]

Published
2013
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47. A Video Browsing Tool for Content Management in Media Post-Production

Author

Bailer, Werner, Weiss, Wolfgang, Schober, Christian, Thallinger, Georg, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Schoeffmann, Klaus, editor, Merialdo, Bernard, editor, Hauptmann, Alexander G., editor, Ngo, Chong-Wah, editor, Andreopoulos, Yiannis, editor, and Breiteneder, Christian, editor

Published
2012
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48. Leveraging CD Gain for Precise Barehand Video Timeline Browsing on Smart Displays

Author

Sachi Mizobuchi, Edward Lank, Wei Li, Wei Zhou, Futian Zhang, and Taslim Arefin Khan

Subjects
Task (computing), Computer science, Gesture recognition, Real-time computing, Word error rate, Timeline, Video browsing, Interval (mathematics), Constant function, Constant (mathematics)
Abstract

One common task when controlling smart displays is the manipulation of video timelines. Given current examples of smart displays that support distant bare hand control, in this paper we explore CD Gain functions to support both seeking and scrubbing tasks. Through a series of experiments, we demonstrate that a linear CD Gain function provides performance advantages when compared to either a constant function or generalised logistic function (GLF). In particular, linear gain is faster than a GLF and has lower error rate than Constant gain. Furthermore, Linear and GLF gains’ average temporal error when targeting a one second interval on a two hour timeline (±5 s) is less than one third the error of a Constant gain.

Published
2021
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49. Scene Walk: a non-photorealistic viewing tool for first-person video

Author

Alan F. Blackwell, Hieu T. Nguyen, Xiaomeng Wang, Richard Jones, Wang, X [0000-0001-9074-1686], and Apollo - University of Cambridge Repository

Subjects
First-person video, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Wearable computer, Cognitive Map, Context (language use), 02 engineering and technology, Virtual reality, Rendering (computer graphics), Computer graphics, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, ComputingMethodologies_COMPUTERGRAPHICS, Cognitive map, business.industry, Camera trajectory, 020207 software engineering, Computer Graphics and Computer-Aided Design, Body-worn camera, Visualization, Human-Computer Interaction, 020201 artificial intelligence & image processing, Video browsing, Artificial intelligence, 3D scene reconstruction, business, Video viewing, Software
Abstract

Scene Walk is a video viewing technique suited to first-person video recorded from wearable cameras. It integrates a 2D video player and visualisation of the camera trajectory into a non-photorealistic partial rendering of the 3D environment as reconstructed from image content. Applications include forensic analysis of first-person video archives, for example as recorded by emergency response teams. The Scene Walk method is designed to support the viewer’s construction and application of a cognitive map of the context in which first-person video was captured. We use methods from wayfinding research to assess the effectiveness of this non-photorealistic approach in comparison to actual physical experience of the scene. We find that Scene Walk does allow viewers to create a more accurate and effective cognitive map of first-person video than is achieved using a conventional video browsing interface and that this model is comparable to actually walking through the original environment.

Published
2021

50. Multiscale Browsing through Video Collections in Smartphones Using Scalable Storyboards.

Author

Herranz, Luis

Abstract

This paper explores how multiscale browsing can be integrated with smart phone interfaces to provide enhanced navigation through video collections. We propose a system that allows the user to interactively change the scale of the storyboards, so the user can easily adjust the amount of information provided by them. Three different methods to select key frames are studied, including an efficient method that analyzes the video and creates a scalable description, with very little computational cost. Then, storyboards of any length can be retrieved on demand without any further analysis, which is very convenient to provide fast multiscale navigation. Experimental evaluations show how this method improves the utility of the summaries and enhances user experience. [ABSTRACT FROM PUBLISHER]

Published
2012
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