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404 results on '"Video indexing"'

2. Efficient feature based video retrieval and indexing using pattern change with invariance algorithm.

Author

Namala, Vasu and Karuppusamy, S. Anbu

Subjects
*VIDEOS, *FEATURE extraction, *TAGS (Metadata), *GRAPH labelings, *FUZZY logic, *ALGORITHMS
Abstract

The amount of audio visual content kept in networked repositories has increased dramatically in recent years. Many video hosting websites exist, such as YouTube, Metacafe, and Google Video. Currently, indexing and categorising these videos is a time-consuming task. The system either asks the user to provide tags for the videos they submit, or manual labelling is used. The aim of this research is to develop a classifier that can accurately identify videos. Every video has content that is either visual, audio, or text. Researchers categorised the videos based on any of these three variables. With the Pattern Change with Size Invariance (PCSI) algorithm, this study provides a hybrid model that takes into account all three components of the video: audio, visual, and textual content. This study tries to classify videos into broad categories such as education, sports, movies, and amateur videos. Key feature extraction and pattern matching would be used to accomplish this. A fuzzy logic and ranking system would be used to assign the tag to the video. The proposed system is tested only on a virtual device in addition a legitimate distributed cluster for the aim of reviewing real-time performance, especially once the amount and duration of films are considerable. The efficiency of video retrieval is measured with metrics like accuracy, precision, and recall is over 99% success. [ABSTRACT FROM AUTHOR]

Published
2023
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3. Automatic Event Detection in User-Generated Video Content: A Survey

Author

Susmitha, Alamuru, Jain, Sanjay, Mohnaty, Mihir Narayan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Mallick, Pradeep Kumar, editor, Bhoi, Akash Kumar, editor, Chae, Gyoo-Soo, editor, and Kalita, Kanak, editor

Published
2021
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4. An Innovative Approach for Aerial Video Surveillance Using Video Content Analysis and Indexing

Author

Jacob, Jaimon, Sudheep Elayidom, M., Devassia, V. P., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Chen, Joy Iong-Zong, editor, Tavares, João Manuel R. S., editor, Shakya, Subarna, editor, and Iliyasu, Abdullah M., editor

Published
2021
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5. Recapitulating the Violence Detection Systems

Author

Jaiswal, Snehil G., Mohod, Sharad W., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Kumar, Amit, editor, and Mozar, Stefan, editor

Published
2020
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6. Index Point Detection and Semantic Indexing of Videos—A Comparative Review

Author

Mahrishi, Mehul, Morwal, Sudha, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pant, Millie, editor, Kumar Sharma, Tarun, editor, Arya, Rajeev, editor, Sahana, B.C., editor, and Zolfagharinia, Hossein, editor

Published
2020
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7. Multi-Object Semantic Video Detection and Indexing Using a 3D Deep Learning Model.

Author

Mofreh, Eslam, Abozeid, Amr, Farouk, Hesham, and ElDahshan, Kamal A.

Subjects
DEEP learning, VIDEOS, OBJECT recognition (Computer vision), FRAMES (Linguistics), SOCIAL media
Abstract

Within the exponential growth in raw data production, attributed in no small part to social media - Facebook, Youtube, and others. Video is proving to be the most important data type thanks to the substantial amount of raw data it contains, requiring an efficient way to be understood, organized, structured, and stored for ease of retrieval. Hence, an efficient video indexing architecture is thus crucial for video datasets. This paper proposes an efficient Multi-Object Semantic Video Detection (MOSD) that leverages the deep learning power to achieve effective indexing on the semantic concept level. MOSD is multi-detection network of video semantics in multiple frames. MOSD exploits a 3D convolution operation to do multiple detections among multiple frames with higher performance. The detected semantics then structured and used for indexing the video segments. MOSD has been trained and evaluated on ImageNet VID dataset and has been compared to peers. MOSD showed efficiency in exploiting the temporal context of a video to do simultaneous detections of consecutive frames which speeds up the detection of semantic objects. MOSD also showed performance efficiency in terms of mAP which is 85.2%. [ABSTRACT FROM AUTHOR]

Published
2022
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8. Microservices Architecture for Content-Based Indexing of Video Shots

Author

Baran, Remigiusz, Partila, Pavol, Wilk, Rafał, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Choroś, Kazimierz, editor, Kopel, Marek, editor, Kukla, Elżbieta, editor, and Siemiński, Andrzej, editor

Published
2019
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9. Utilizing Deep Object Detector for Video Surveillance Indexing and Retrieval

Author

Durand, Tom, He, Xiyan, Pop, Ionel, Robinault, Lionel, 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, Kompatsiaris, Ioannis, editor, Huet, Benoit, editor, Mezaris, Vasileios, editor, Gurrin, Cathal, editor, Cheng, Wen-Huang, editor, and Vrochidis, Stefanos, editor

Published
2019
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11. Shot Boundary Detection with Spatial-Temporal Convolutional Neural Networks

Author

Wu, Lifang, Zhang, Shuai, Jian, Meng, Zhao, Zhijia, Wang, Dong, 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, Lai, Jian-Huang, editor, Liu, Cheng-Lin, editor, Chen, Xilin, editor, Zhou, Jie, editor, Tan, Tieniu, editor, Zheng, Nanning, editor, and Zha, Hongbin, editor

Published
2018
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12. Monitoring Interactions

Author

Meißner, Felix, Veltkamp, Remco C., Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin Sherman, Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert Y., Series editor, Poppe, Ronald, editor, Meyer, John-Jules, editor, Veltkamp, Remco, editor, and Dastani, Mehdi, editor

Published
2017
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13. INDEXED DATASET FROM YOUTUBE FOR A CONTENT-BASED VIDEO SEARCH ENGINE.

Author

Adly, Ahmad Sedky, Hegazy, Islam, Elarif, Taha, and Abdelwahab, M. S.

Subjects
SEARCH engines, STREAMING video & television, BIG data, COMPUTER vision
Abstract

Numerous researches on content-based video indexing and retrieval besides video search engines are tied to a large-scaled video dataset. Unfortunately, reduction in open-sourced datasets resulted in complications for novel approaches exploration. Although, video datasets that index video files located on public video streaming services have other purposes, such as annotation, learning, classification, and other computer vision areas, with little interest in indexing public video links for purpose of searching and retrieval. This paper introduces a novel large-scaled dataset based on YouTube video links to evaluate the proposed content-based video search engine, gathered 1088 videos, that represent more than 65 hours of video, 11,000 video shots, and 66,000 unmarked and marked keyframes, 80 different object names used for marking. Moreover, a state-of-the-art features vector, and combinational-based matching, beneficial to the accuracy, speed, and precision of the video retrieval process. Any video record in the dataset is represented by three features: temporal combination vector, object combination vector with shot annotations, and 6 keyframes, sideways with other metadata. Video classification for the dataset was also imposed to expand the efficiency of retrieval of video-based queries. A two-phased approach has been used based on object and event classification, storing video records in aggregations related to feature vectors extracted. While object aggregation stores video records with the maximal occurrence of extracted object/concept from all shots, event aggregation classify based on groups according to the number of shots per video. This study indexed 58 out of 80 different object/concept categories, each has 9 shot number groups. [ABSTRACT FROM AUTHOR]

Published
2021
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14. Video content analysis and retrieval system using video storytelling and indexing techniques.

Author

Jacob, Jaimon, Elayidom, M. Sudheep, and Devassia, V. P.

Subjects
VIDEOS, CONTENT analysis, SYSTEM analysis, SEARCH algorithms, WORD frequency, VIDEO excerpts
Abstract

Videos are used often for communicating ideas, concepts, experience, and situations, because of the significant advances made in video communication technology. The social media platforms enhanced the video usage expeditiously. At, present, recognition of a video is done, using the metadata like video title, video descriptions, and video thumbnails. There are situations like video searcher requires only a video clip on a specific topic from a long video. This paper proposes a novel methodology for the analysis of video content and using video storytelling and indexing techniques for the retrieval of the intended video clip from a long duration video. Video storytelling technique is used for video content analysis and to produce a description of the video. The video description thus created is used for preparation of an index using wormhole algorithm, guarantying the search of a keyword of definite length L, within the minimum worst-case time. This video index can be used by video searching algorithm to retrieve the relevant part of the video by virtue of the frequency of the word in the keyword search of the video index. Instead of downloading and transferring a whole video, the user can download or transfer the specifically necessary video clip. The network constraints associated with the transfer of videos are considerably addressed. [ABSTRACT FROM AUTHOR]

Published
2020
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15. Joint Multi-View Hashing for Large-Scale Near-Duplicate Video Retrieval.

Author

Nie, Xiushan, Jing, Weizhen, Cui, Chaoran, Zhang, Chen Jason, Zhu, Lei, and Yin, Yilong

Subjects
*HASHING, *STREAMING video & television, *WEB databases, *VIDEOS
Abstract

Multi-view hashing can well support large-scale near-duplicate video retrieval, due to its desirable advantages of mutual reinforcement of multiple features, low storage cost, and fast retrieval speed. However, there are still two limitations that impede its performance. First, existing methods only consider local structures in multiple features. They ignore the global structure that is important for near-duplicate video retrieval, and cannot fully exploit the dependence and complementarity of multiple features. Second, existing works always learn hashing functions bit by bit, which unfortunately increases the time complexity of hash function learning. In this paper, we propose a supervised hashing scheme, termed as joint multi-view hashing (JMVH), to address the aforementioned problems. It jointly preserves the global and local structures of multiple features while learning hashing functions efficiently. Specially, JMVH considers features of video as items, based on which an underlying Hamming space is learned by simultaneously preserving their local and global structures. In addition, a simple but efficient multi-bit hash function learning based on generalized eigenvalue decomposition is devised to learn multiple hash functions within a single step. It can significantly reduce the time complexity of conventional hash function learning processes that sequentially learn multiple hash functions bit by bit. The proposed JMVH is evaluated on two public databases: CC_WEB_VIDEO and UQ_VIDEO. Experimental results demonstrate that the proposed JMVH achieves more than a 5 percent improvement compared to several state-of-the-art methods which indicates the superior performance of JMVH. [ABSTRACT FROM AUTHOR]

Published
2020
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16. Improved Fuzzy-Based SVM Classification System Using Feature Extraction for Video Indexing and Retrieval.

Author

Gayathri, N. and Mahesh, K.

Subjects
SUPPORT vector machines, FEATURE extraction, MULTIMEDIA systems, HISTOGRAMS, FUZZY systems
Abstract

Various researches have been performed with video abstraction with the constant development of multimedia technology. However, there are some deficiencies that have been encountered in the pre-processing of video frames before attaining classified video archives. To overcome the drawbacks in pre-processing, feature extraction and classification approaches are considered. Here, video indexing has been anticipated with several features' extraction with dominant frame generation for the input video frame. Fuzzy-based SVM classifier is utilized to categorize frame set into dominant structures. Multi-dimensional Histogram of Oriented Gradients (HOG) and colour feature extraction are used to extract texture features from the video frame. Using the frame sequence, the vector space of structures is captured; dominant frameworks are utilized in video indexing. Shot transitions' classification is done with a fuzzy system. Experimental outcomes demonstrate that shot boundary detection accuracy increases with an increase in iterations. The simulation was carried out in MATLAB environment. This technique attains an accuracy of about 95.4%, the precision of 100%, and the F1 score of 100% and a recall of 100%. The misclassification rate is 4.6%. The proposed method shows better trade-off than the existing techniques. [ABSTRACT FROM AUTHOR]

Published
2020
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17. Video Retrieval Using Local Binary Pattern

Author

Varma, Satishkumar, Talbar, Sanjay, Howlett, Robert J., Series editor, Jain, Lakhmi C., Series editor, Behera, Himansu Sekhar, editor, Mandal, Jyotsna Kumar, editor, and Mohapatra, Durga Prasad, editor

Published
2015
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18. Fisher Kernel Based Task Boundary Retrieval in Laparoscopic Database with Single Video Query

Author

Twinanda, Andru Putra, De Mathelin, Michel, Padoy, Nicolas, 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, Golland, Polina, editor, Hata, Nobuhiko, editor, Barillot, Christian, editor, Hornegger, Joachim, editor, and Howe, Robert, editor

Published
2014
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19. Video genre identification using clustering-based shot detection algorithm.

Author

Daudpota, Sher Muhammad, Muhammad, Atta, and Baber, Junaid

Abstract

Rapid growth in storage technology and data acquisition has significantly increased the volume of multimedia data online. A challenging problem is to analyze that multimedia data which are in massive quantity. In recent years, indexing of video files based on contents has gained increased popularity in research community. There are also attempts at identifying if a video clip is containing a specific genre of video, e.g., an sports video, movie, drama, animation or talk show. These proposed techniques, however, use a long list of audio visual features in achieving this classification task, which obviously decreases processing efficiency. Based on certain patterns in audio visual features and basic grammar of talk show, this research differentiates multimedia contents of talk shows from rest of the video genres. Our multimodal rule-based classification approach exploits shots and scenes in a video as classification features. The contents from popular multimedia databases like DailyMotion, YouTube and movies from Hollywood and Bollywood are used as dataset to test overall system of genre identification. The system achieves precision and recall of 98% and 100%, respectively, on 600 selected videos of more than 600 h of duration to classify multimedia content as either 'TalkShow' or 'OtherVideo' category. [ABSTRACT FROM AUTHOR]

Published
2019
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20. Solar panel monitoring: real-time system using video watermarking and Mosaicing.

Author

Lafkih, Sara and Zaz, Youssef

Subjects
SOLAR cells, DISCRETE cosine transforms, DIGITAL watermarking, SOLAR energy, SOLAR power plants
Abstract

In this paper, we propose a new approach to monitor a solar plant in real-time using an embedded vision system, comprised mainly of raspberry Pi3 card, GPS and thermometer sensors, and an HD camera module. This approach consists on using image processing techniques on solar energy fields. It is implemented on two steps, the first apply the digital watermarking technique based on discrete cosine transform (DCT) to indexing a captured video. We start by capturing all information from the solar plant (GPS coordinates, temperature, date, and time), then we embed on each video frame their related data. This method depends on transforming the video frames from the spatial to the frequency domain using DCT and embedding data in 8 × 8 block coefficients. The second step consists on applying a mosaicing techniques based on the scale invariant features transform (SIFT), in order to generate a panoramic image of the solar plant from video frames. Remotely, the supervisor can visualize the image of the whole, part, or panel of the solar plant, and exploits the embedded data to ensure an efficient real-time monitoring. The proposed method allows a high efficiency in terms of accuracy, data security, and retrieving data quickly, also it offers a high capacity, less imperceptibility and good robustness. [ABSTRACT FROM AUTHOR]

Published
2019
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21. Histogram difference with Fuzzy rule base modeling for gradual shot boundary detection in video cloud applications.

Author

Kethsy Prabavathy, A. and Devi Shree, J.

Subjects
*VIDEO compression, *FUZZY sets, *HISTOGRAMS, *SYSTEM identification, *VIDEOS, *CONTENT analysis
Abstract

In the field of shot boundary detection the fundamental step is video content analysis towards video indexing, summarization and retrieval as to be carried out for video cloud based applications. However, there are several beneficial in the previous work; reliable detection of video shot is still a challenging issue. In this paper the focus is carried out on the problem of gradual transition detection from video. The proposed approach is fuzzy-rule based system with gradual identification and a set of fuzzy rules are evaluated with dissolve and wipes (fad-in and fad-out) during gradual transition. First, extracting the features from the video frames then applying the fuzzy rules in to the frames for identifying the gradual transitions. The main advantage of the proposed method is its level of accuracy in the gradual detection getting increased. Also, the existing gradual detection algorithms are mainly based on the threshold component, but the proposed method is rule based. The proposed method is evaluated on variety of video sequences from different genres and compared with existing techniques from the literature. Experimental results proved for its effectiveness on calculating performance in terms of the precision and recall rates. [ABSTRACT FROM AUTHOR]

Published
2019
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22. New fusional framework combining sparse selection and clustering for key frame extraction

Author

Mengjuan Fei, Wei Jiang, Weijie Mao, and Zhendong Song

Subjects
fusional framework, sparse selection, video indexing, video content preservation, mutual information based agglomerative hierarchical clustering, optimal key frame, Computer applications to medicine. Medical informatics, R858-859.7, Computer software, QA76.75-76.765
Abstract

Key frame extraction can facilitate rapid browsing and efficient video indexing in many applications. However, to be effective, key frames must preserve sufficient video content while also being compact and representative. This study proposes a syncretic key frame extraction framework that combines sparse selection (SS) and mutual information‐based agglomerative hierarchical clustering (MIAHC) to generate effective video summaries. In the proposed framework, the SS algorithm is first applied to the original video sequences to obtain optimal key frames. Then, using content‐loss minimisation and representativeness ranking, several candidate key frames are efficiently selected and grouped as initial clusters. A post‐processor – an improved MIAHC – subsequently performs further processing to eliminate redundant images and generate the final key frames. The proposed framework overcomes issues such as information redundancy and computational complexity that afflict conventional SS methods by first obtaining candidate key frames instead of accurate key frames. Subsequently, application of the improved MIAHC to these candidate key frames rather than the original video not only results in the generation of accurate key frames, but also reduces the computation time for clustering large videos. The results of comparative experiments conducted on two benchmark datasets verify that the performance of the proposed SS–MIAHC framework is superior to that of conventional methods.

Published
2016
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24. Lecture Video Browsing Using Multimodal Information Resources

Author

Yang, Haojin, Grünewald, Franka, Bauer, Matthias, Meinel, Christoph, 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, Wang, Jhing-Fa, editor, and Lau, Rynson, editor

Published
2013
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25. Unsupervised Visual Changepoint Detection Using Maximum Mean Discrepancy

Author

Diu, Michael, Gangeh, Mehrdad, Kamel, Mohamed S., 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, Kamel, Mohamed, editor, and Campilho, Aurélio, editor

Published
2013
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28. An Automated Analysis and Indexing Framework for Lecture Video Portal

Author

Yang, Haojin, Oehlke, Christoph, Meinel, Christoph, 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, Popescu, Elvira, editor, Li, Qing, editor, Klamma, Ralf, editor, Leung, Howard, editor, and Specht, Marcus, editor

Published
2012
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29. Content-Based Video Description for Automatic Video Genre Categorization

Author

Ionescu, Bogdan, Seyerlehner, Klaus, Rasche, Christoph, Vertan, Constantin, Lambert, Patrick, 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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30. Retrieval of Multiple Instances of Objects in Videos

Author

Bursuc, Andrei, Zaharia, Titus, Prêteux, Françoise, 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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32. A Contour-Color-Action Approach to Automatic Classification of Several Common Video Genres

Author

Ionescu, Bogdan E., Rasche, Christoph, Vertan, Constantin, Lambert, Patrick, 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, Detyniecki, Marcin, editor, Knees, Peter, editor, Nürnberger, Andreas, editor, Schedl, Markus, editor, and Stober, Sebastian, editor

Published
2011
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33. A Video Grammar-Based Approach for TV News Localization and Intra-structure Identification in TV Streams

Author

Zlitni, Tarek, Mahdi, Walid, Ben-Abdallah, Hanène, 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, Maino, Giuseppe, editor, and Foresti, Gian Luca, editor

Published
2011
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35. An Effective Video Text Tracking Algorithm Based on SIFT Feature and Geometric Constraint

Author

Na, Yinan, Wen, Di, 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, Qiu, Guoping, editor, Lam, Kin Man, editor, Kiya, Hitoshi, editor, Xue, Xiang-Yang, editor, Kuo, C.-C. Jay, editor, and Lew, Michael S., editor

Published
2010
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36. Efficient Bulk-Insertion for Content-Based Video Indexing

Author

Onkhum, Narissa, Natwichai, Juggapong, 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, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Kang, Byeong-Ho, editor, and Richards, Debbie, editor

Published
2010
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37. Learning a Multi-Concept Video Retrieval Model with Multiple Latent Variables.

Author

MAZAHERI, AMIR, GONG, BOQING, and SHAH, MUBARAK

Subjects
VIDEOS, DETECTORS, IMAGE retrieval, MULTIMEDIA systems, DIGITAL media
Abstract

Effective and efficient video retrieval has become a pressing need in the "big video" era. The objective of this work is to provide a principled model for computing the ranking scores of a video in response to one or more concepts, where the concepts could be directly supplied by users or inferred by the system from the user queries. Indeed, how to deal with multi-concept queries has become a central component in modern video retrieval systems that accept text queries. However, it has been long overlooked and simply implemented by weighted averaging of the corresponding concept detectors' scores. Our approach, which can be considered as a latent ranking SVM, integrates the advantages of various recent works in text and image retrieval, such as choosing ranking over structured prediction, modeling inter-dependencies between querying concepts, and so on. Videos consist of shots, and we use latent variables to account for the mutually complementary cues within and across shots. Concept labels of shots are scarce and noisy. We introduce a simple and effective technique to make our model robust to outliers. Our approach gives superior performance when it is tested on not only the queries seen at training but also novel queries, some of which consist of more concepts than the queries used for training. [ABSTRACT FROM AUTHOR]

Published
2018
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38. Improving the robustness of motion vector temporal descriptor.

Author

Rahmani, Farzaneh, Zargari, Farzad, and Ghanbari, Mohammad

Abstract

Motion vectors (MVs) are the most common temporal descriptors in video analysis, indexing and retrieval applications. However, video indexing and analysis based on MVs do not perform well for videos at different dimension ratios (DRs) or even various resolutions. As a result, video indexing and analysis which are based on identifying similar video face with many difficulties at different DRs or resolutions by MVs. In this study, a two‐stage algorithm is introduced to make MV descriptors robust against variations first in DR and then at resolution. In the experiments performed on motion vector histograms, the proposed method improves the performance on identifying similar videos at various spatial specifications by up to 73%. Moreover, in the video retrieval experiments, the proposed modified MV outperforms original MV feature vector. This is an indication of improvement in differentiation of similar and dissimilar videos by the proposed temporal feature vector. [ABSTRACT FROM AUTHOR]

Published
2018
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39. Unsupervised Detection of Gradual Video Shot Changes with Motion-Based False Alarm Removal

Author

Ewerth, Ralph, Freisleben, Bernd, 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, Blanc-Talon, Jacques, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2009
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40. Performance Prediction for Unsupervised Video Indexing

Author

Ewerth, Ralph, Freisleben, Bernd, 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, Jiang, Xiaoyi, editor, and Petkov, Nicolai, editor

Published
2009
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41. A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition

Author

Dubois, Sloven, Péteri, Renaud, Ménard, Michel, 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, Araujo, Helder, editor, Mendonça, Ana Maria, editor, Pinho, Armando J., editor, and Torres, María Inés, editor

Published
2009
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42. A New Global Alignment Method for Feature Based Image Mosaicing

Author

Elibol, A., Garcia, R., Delaunoy, O., Gracias, N., 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, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Remagnino, Paolo, editor, Porikli, Fatih, editor, Peters, Jörg, editor, Klosowski, James, editor, Arns, Laura, editor, Chun, Yu Ka, editor, Rhyne, Theresa-Marie, editor, and Monroe, Laura, editor

Published
2008
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43. Unsupervised Video Shot Segmentation Using Global Color and Texture Information

Author

Chang, Yuchou, Lee, Dah-Jye, Hong, Yi, Archibald, James, 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, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Remagnino, Paolo, editor, Porikli, Fatih, editor, Peters, Jörg, editor, Klosowski, James, editor, Arns, Laura, editor, Chun, Yu Ka, editor, Rhyne, Theresa-Marie, editor, and Monroe, Laura, editor

Published
2008
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44. Semantic Video Indexing

Author

Snoek, Cees G. M., Worring, Marcel, Geusebroek, Jan-Mark, Koelma, Dennis C., Seinstra, Frank J., Smeulders, Arnold W. M., Carey, M. J., editor, Ceri, S., editor, Bernstein, P., editor, Dayal, U., editor, Faloutsos, C., editor, Freytag, J. C., editor, Gardarin, G., editor, Jonker, W., editor, Krishnamurthy, V., editor, Neimat, M. -A., editor, Valduriez, P., editor, Weikum, G., editor, Whang, K. -Y., editor, Widom, J., editor, Blanken, Henk M., editor, Blok, Henk Ernst, editor, Feng, Ling, editor, and de Vries, Arjen P., editor

Published
2007
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47. Extraction of Motion Activity from Scalable-Coded Video Sequences

Author

Herranz, Luis, Tiburzi, Fabricio, Bescós, Jesús, 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, Avrithis, Yannis, editor, Kompatsiaris, Yiannis, editor, Staab, Steffen, editor, and O’Connor, Noel E., editor

Published
2006
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48. Video Indexing and Retrieval in Compressed Domain Using Fuzzy-Categorization

Author

Fang, Hui, Qahwaji, Rami, Jiang, Jianmin, 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, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Remagnino, Paolo, editor, Nefian, Ara, editor, Meenakshisundaram, Gopi, editor, Pascucci, Valerio, editor, Zara, Jiri, editor, Molineros, Jose, editor, Theisel, Holger, editor, and Malzbender, Tom, editor

Published
2006
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49. Video Spatio-temporal Signatures Using Polynomial Transforms

Author

Rivero-Moreno, Carlos Joel, Bres, Stéphane, 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, Bres, Stéphane, editor, and Laurini, Robert, editor

Published
2006
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50. DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval

Author

Giorgos Kordopatis-Zilos, Christos Tzelepis, Ioannis (Yiannis) Kompatsiaris, Symeon Papadopoulos, and Ioannis Patras

Subjects
FOS: Computer and information sciences, similarity learning, Computer Vision and Pattern Recognition (cs.CV), video similarity, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval, Multimedia (cs.MM), video retrieal, Artificial Intelligence, Computer Vision and Pattern Recognition, video indexing, Computer Science - Multimedia, Software, Information Retrieval (cs.IR)
Abstract

In this paper, we address the problem of high performance and computationally efficient content-based video retrieval in large-scale datasets. Current methods typically propose either: (i) fine-grained approaches employing spatio-temporal representations and similarity calculations, achieving high performance at a high computational cost or (ii) coarse-grained approaches representing/indexing videos as global vectors, where the spatio-temporal structure is lost, providing low performance but also having low computational cost. In this work, we propose a Knowledge Distillation framework, called Distill-and-Select (DnS), that starting from a well-performing fine-grained Teacher Network learns: (a) Student Networks at different retrieval performance and computational efficiency trade-offs and (b) a Selector Network that at test time rapidly directs samples to the appropriate student to maintain both high retrieval performance and high computational efficiency. We train several students with different architectures and arrive at different trade-offs of performance and efficiency, i.e., speed and storage requirements, including fine-grained students that store/index videos using binary representations. Importantly, the proposed scheme allows Knowledge Distillation in large, unlabelled datasets—this leads to good students. We evaluate DnS on five public datasets on three different video retrieval tasks and demonstrate (a) that our students achieve state-of-the-art performance in several cases and (b) that the DnS framework provides an excellent trade-off between retrieval performance, computational speed, and storage space. In specific configurations, the proposed method achieves similar mAP with the teacher but is 20 times faster and requires 240 times less storage space. The collected dataset and implementation are publicly available: https://github.com/mever-team/distill-and-select.

Published
2022
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