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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]

Video containment queries find the videos that have similar sequence of frames to query video clips. Applying sequence matching to all possible subsequences for video containment queries is computationally expensive for large volumes of video data. In this paper, we propose an efficient candidate segment selection scheme, which selects only a small set of subsequences to be matched to the query sequence, by using a cluster of similar frames, called a frame cluster. We also propose a new type of the ordinal feature, called a composite ordinal feature that allows multiple ranks to certain cells. In experiments with large scale video data sets, we show our method improves the query response time by efficiently selecting a set of subsequences for sequence matching. [ABSTRACT FROM AUTHOR]

A video retrieval system user hopes to find relevant information when the proposed queries are ambiguous. The retrieval process based on detecting concepts remains ineffective in such a situation. Potential relationships between concepts have been shown as a valuable knowledge resource that can enhance the retrieval effectiveness, even for ambiguous queries. Recent researches in multimedia retrieval have focused on ontology modeling as a common framework to manage knowledge. Handling these ontologies has to cope with issues related to generic knowledge management and processing scalability. Considering these issues, we suggest a context-based fuzzy ontology framework for video content analysis and indexing. In this paper, we focused on the way in which we modeled our fuzzy ontology: First, we populate automatically the generated ontology by gathering various available video annotation datasets. Then, the ontology content was used to infer enhanced video semantic interpretation. Finally, considering user feedback, the content of the ontology was improved. Experimental results showed that our approach achieves the goal of scalability while at the same time allowing better video content semantic interpretation. [ABSTRACT FROM AUTHOR]

This paper presents a novel multimedia information system, called SAPTE, for supporting the discourse analysis and information retrieval of television programs from their corresponding video recordings. Unlike most common systems, SAPTE uses both content independent and dependent metadata, which are determined by the application of discourse analysis techniques as well as image and audio analysis methods. The proposed system was developed in partnership with the free-to-air Brazilian TV channel Rede Minas in an attempt to provide TV researchers with computational tools to assist their studies about this media universe. The system is based on the Matterhorn framework for managing video libraries, combining: (1) discourse analysis techniques for describing and indexing the videos, by considering aspects, such as, definitions of the subject of analysis, the nature of the speaker and the corpus of data resulting from the discourse; (2) a state of the art decoder software for large vocabulary continuous speech recognition, called Julius; (3) image and frequency domain techniques to compute visual signatures for the video recordings, containing color, shape and texture information; and (4) hashing and k-d tree methods for data indexing. The capabilities of SAPTE were successfully validated, as demonstrated by our experimental results, indicating that SAPTE is a promising computational tool for TV researchers. [ABSTRACT FROM AUTHOR]

This paper presents a method to automatically detect slide changes in lecture videos. For accurate detection, the regions capturing slide images are first identified from video frames. Then, SIFT features are extracted from the regions, which are invariant to image scaling and rotation. These features are used to compare similarity between frames. If the similarity is smaller than a threshold, slide transition is detected. The threshold is estimated based on the mean and standard deviation of sample frames' similarities. Using this method, high detection accuracy can be obtained without any supplementary slide images. The proposed method also supports detection of backward slide transitions that occur when a speaker returns to a previous slide to emphasize its contents. In experiments conducted on our test collection, the proposed method showed 87 % accuracy in forward transition detection and 86 % accuracy in backward transition detection. [ABSTRACT FROM AUTHOR]

Video indexing is employed to represent the features of video sequences. Motion vectors derived from compressed video are preferred for video indexing because they can be accessed by partial decoding; thus, they are used extensively in various video analysis and indexing applications. In this study, we introduce an efficient compressed domain video indexing method and implement it on the H.264/AVC coded videos. The video retrieval experimental evaluations indicate that the video retrieval based on the proposed indexing method outperforms motion vector based video retrieval in 74 % of queries with little increase in computation time. Furthermore, we compared our method with a pixel level video indexing method which employs both temporal and spatial features. Experimental evaluation results indicate that our method outperforms the pixel level method both in performance and speed. Hence considering the speed and precision characteristics of indexing methods, the proposed method is an efficient indexing method which can be used in various video indexing and retrieval applications. [ABSTRACT FROM AUTHOR]

This paper presents a method for indexing activities of daily living in videos acquired from wearable cameras. It addresses the problematic of analyzing the complex multimedia data acquired from wearable devices, which has been recently a growing concern due to the increasing amount of this kind of multimedia data. In the context of dementia diagnosis by doctors, patient activities are recorded in the environment of their home using a lightweight wearable device, to be later visualized by the medical practitioners. The recording mode poses great challenges since the video data consists in a single sequence shot where strong motion and sharp lighting changes often appear. Because of the length of the recordings, tools for an efficient navigation in terms of activities of interest are crucial. Our work introduces a video structuring approach that combines automatic motion based segmentation of the video and activity recognition by a hierarchical two-level Hidden Markov Model. We define a multi-modal description space over visual and audio features, including mid-level features such as motion, location, speech and noise detections. We show their complementarities globally as well as for specific activities. Experiments on real data obtained from the recording of several patients at home show the difficulty of the task and the promising results of the proposed approach. [ABSTRACT FROM AUTHOR]

Music and songs are integral parts of Bollywood movies. Every movie of two to three hours, contains three to ten songs, each song is 3-10 min long. Music lovers like to listen music and songs of a movie, however it is time consuming and error prone to search manually all the songs in a movie. Moreover, the task becomes much harder when songs are to be extracted from a huge archived movies' database containing hundreds of movies. This paper presents an approach to automatically extract music and songs from archived musical movies. We used song grammar to construct Markov Chain Model that differentiates song scenes from dialogue and action scenes in a movie. We tested our system on Bollywood, Hollywood, Pakistani, Bengali, and Tamil movies. A total of 20 movies from different industries were selected for the experiments. On Bollywood movies, we achieved 97.22% recall in song extraction, whereas the recall on Hollywood musical movies is 80%. The test result on Pakistani, Tamil and Bengali movies is 87.09%. [ABSTRACT FROM AUTHOR]

Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR ( Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets. [ABSTRACT FROM AUTHOR]

Audio-Visual People Diarization (AVPD) is an original framework that simultaneously improves audio, video, and audiovisual diarization results. Following a literature review of people diarization for both audio and video content and their limitations, which includes our own contributions, we describe a proposed method for associating both audio and video information by using co-occurrence matrices and present experiments which were conducted on a corpus containing TV news, TV debates, and movies. Results show the effectiveness of the overall diarization system and confirm the gains audio information can bring to video indexing and vice versa. [ABSTRACT FROM AUTHOR]

Video information processing and retrieval is a key aspect of future multimedia technologies and applications. Commercial videos encode several planes of expression through a rich and dense use of colors, editing effects, viewpoints and rhythms, which are exploited together to attract potential purchasers. Databases of commercials can be accessed in order to analyze how a commercial has been developed, retrieve commercials similar to an example, catalog commercials according to the kind of message conveyed to the user. In this paper, we present a system allowing the retrieval of commercial streams based on their salient semantics. Semantics is regarded from the semiotics perspective: collections of signs and semantic features like colors, editing effects, motion, etc. are used as basic blocks with which the meaning of a commercial is constructed. In our system, it is possible to retrieve commercials according to both the meaning they convey and to their similarity to examples. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of similarity between video sequences. Three basic questions are raised and (partially) answered. Firstly, at what temporal duration can video sequences be compared? The frame, shot, scene and video levels are identified. Secondly, given some image or video feature, what are the requirements on its distance measure and how can it be “easily” transformed into the visual similarity desired by the inquirer? Thirdly, how can video sequences be compared at different levels? A general approach based on either a set or sequence representation with variable degrees of aggregation is proposed and applied recursively over the different levels of temporal resolution. It allows the inquirer to fully control the importance of temporal ordering and duration. The general approach is illustrated by introducing and discussing some of the many possible image and video features. Promising experimental results are presented. [ABSTRACT FROM AUTHOR]

This paper presents the use of video parsing and video analysis techniques to attain a radical change in Content Analysis processes. Content Analysis provides frameworks for a better understanding of information, mass media information in particular. The goals of the work presented in this paper are to assess the feasibility of designing Content Analysis processes under much faster and much less manpowered conditions, to prove the power and usefulness of some video parsing and analysis techniques under current inexpensive and widely available software and hardware platforms, and to emphasize that the characteristics of a given situated use of a technological artifact often provide radical simplifications to the system requirements, and make it feasible today. [ABSTRACT FROM AUTHOR]

Video partitioning is the segmentation of a videosequence into visually independent partitions,which represent various identifiable scenes in thevideo. It is an important first step inconsidering other issues in video databasesmanagement, such as indexing and retrieval. Asvideo partitioning is a computationally intensiveprocess, effective management of digital videorequires highly efficient techniques for theprocess. In general, for compressed anduncompressed video, the basic mechanism used toreduce computation is by selective processing of asubpart of the video frames. However, so farthe choice of this proportion has been maderandomly, without any formal basis. An ad hocselection of this subpart cannot always guaranteea reduction in computation while ensuringeffective partitioning. This paper presents formal methods for determiningthe optimal window size and the minimum thresholdswhich ensure that decisions on scene similarityare made on a reliable, effective and principledbasis. Further, we propose the use ofneighbourhood-based colour ratios, and derive theratio feature for both uncompressed and transformcoded video. The neighbourhood-based ratiofeatures account for both illumination variationand possible motion in the video, while avoidingthe computational burden of explicit motioncompensation procedures. Empirical results showing the performance of the proposed techniques are are also presented. [ABSTRACT FROM AUTHOR]

In this paper we describe the Virtual Video Browser (VVB) software application designed to allow the interactive browsing and content-based query of a video database and to facilitate the subsequent playout of selected titles. The VVB is a manifestation of our mechanisms for the location, identification, and delivery of digital audio and video in a distributed system which can be extended to several application domains including multimedia-based home entertainment, catalog shopping, and distance learning. The VVB employs a two phase retrieval process to serve its users. In the query phase, user queries are sent to a metadata server for processing. In the subsequent playout phase, a connection is established between the client workstation and a video server for the delivery of video data. The VVB incorporates a simple query interface that lets users specify their preferences to the system and retrieve the appropriate video. The application is designed to work in a distributed environment where video sequences are stored in different databases interconnected via a network. It has been shown to be a viable target application useful for investigating research problems related to building interactive multimedia systems. [ABSTRACT FROM AUTHOR]

Effective and efficient tools for segmenting and content-based indexing of digital video are essential to allow easy access to video-based information. Most existing segmentation techniques do not use explicit models of video. The approach proposed here is inspired and influenced by well established video production processes. Computational models of these processes are developed. The video models are used to classify the transition effects used in video and to design automatic edit effect detection algorithms. Video segmentation has been formulated as a production model based classification problem. The video models are also used to define segmentation error measures. Experimental results from applying the proposed technique to commercial cable television programming are presented. [ABSTRACT FROM AUTHOR]