Fast evolution of Internet technologies has led to an explosive growth of video data available in the public domain and created unprecedented challenges in the analysis, organization, management, and control of such content. The problems encountered in video analysis such as identifying a video in a large database (e.g. detecting pirated content in YouTube), putting together video fragments, finding similarities and common ancestry between different versions of a video, have analogous counterpart problems in genetic research and analysis of DNA and protein sequences. In this paper, we exploit the analogy between genetic sequences and videos and propose an approach to video analysis motivated by genomic research. Representing video information as video DNA sequences and applying bioinformatic algorithms allows to search, match, and compare videos in large-scale databases. We show an application for content-based metadata mapping between versions of annotated video.
One of the cornerstone problems in content-based copy detection and search is the creation of a video representation that would allow to compare and match videos across versions. Different representations based on mosaic @cite_28 , shot boundaries @cite_1 , motion, color, and spatio-temporal intensity distribution @cite_29 , color histograms @cite_15 , and ordinal measure @cite_22 , were proposed. When considering large variability of versions due to post-production modifications, methods based on spatial @cite_17 @cite_13 @cite_25 and spatio-temporal @cite_23 points of interest and local descriptors were shown to be advantageous @cite_27 . In addition, these methods proved to be very efficient in image search in very large databases @cite_16 @cite_20 . More recently, Willems @cite_18 proposed feature-based spatio-temporal video descriptors combining both visual information of single video frames as well as the temporal relations between subsequent frames.
@cite_18 @cite_22 @cite_28 @cite_29 @cite_1 @cite_27 @cite_23 @cite_15 @cite_16 @cite_13 @cite_25 @cite_20 @cite_17
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n this paper, we present a new method for robust content-based video copy detection based on local spatio-temporal features. As we show by experimental validation, the use of local spatio-temporal features instead of purely spatial ones brings additional robustness and discriminativity. Efficient operation is ensured by using the new spatio-temporal features proposed in [20]. To cope with the high-dimensionality of the resulting descriptors, these features are incorporated in a disk-based index and query system based on p-stable locality sensitive hashing. The system is applied to the task of video footage reuse detection in news broadcasts. Results are reported on 88 hours of news broadcast data from the TRECVID2006 dataset. This paper presents an efficient approach for copies detection in a large videos archive consisting of several hundred of hours. The video content indexing method consists of extracting the dynamic behavior on the local description of interest points and further on the estimation of their trajectories along the video sequence. Analyzing the low-level description obtained allows to highlight trends of behaviors and then to assign a label of behavior to each local descriptor. Such an indexing approach has several interesting properties: it provides a rich, compact and generic description, while labels of behavior provide a high-level description of the video content. Here, we focus on video Content Based Copy Detection (CBCD). Copy detection is problematic as similarity search problem but with prominent differences. To be efficient, it requires a dedicated on-line retrieval method based on a specific voting function. This voting function must be robust to signal transformations and discriminating versus high similarities which are not copies. The method we propose in this paper is a dedicated on-line retrieval method based on a combination of the different dynamic contexts computed during the off-line indexing. A spatio-temporal registration based on the relevant combination of detected labels is then applied. This approach is evaluated using a huge video database of 300 hours with different video tests. The method is compared to a state-of-the art technique in the same conditions. We illustrate that taking labels into account in the specific voting process reduces false alarms significantly and drastically improves the precision. A video copy detection system is a content-based search engine [1]. It aims at deciding whether a query video segment is a copy of a video from the indexed dataset or not. A copy may be distorted in various ways. If the system finds a matching video segment, it returns the name of the database video and the time stamp where the query was copied from. Fig. 1 illustrates the video copyright detection system we have developed for the TRECVID 2008 evaluation campaign. The components of this system are detailed in Section 2. Most of them are derived from the state-of-the-art image search engine introduced in [2]. It builds upon the bag-of-features image search system proposed in [3], and provides a more precise representation by adding 1) a Hamming embedding and 2) weak geometric consistency constraints. The HE provides binary signatures that refine the visual word based matching. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within an inverted file and are efficiently exploited for all indexed frames, even for a very large dataset. In our best runs, we have indexed 2 million keyframes, represented by 800 million local descriptors. We give some conclusions drawn from our experiments in Section 3. Finally, in section 4 we briefly present our run for the high-level feature detection task. To detect violence in a video, a common video description method is to apply local spatio-temporal description on the query video. Then, the low-level description is further summarized onto the high-level feature based on Bag-of-Words (BoW) model. However, traditional spatio-temporal descriptors are not discriminative enough. Moreover, BoW model roughly assigns each feature vector to only one visual word, therefore inevitably causing quantization error. To tackle the constrains, this paper employs Motion SIFT (MoSIFT) algorithm to extract the low-level description of a query video. To eliminate the feature noise, Kernel Density Estimation (KDE) is exploited for feature selection on the MoSIFT descriptor. In order to obtain the highly discriminative video feature, this paper adopts sparse coding scheme to further process the selected MoSIFTs. Encouraging experimental results are obtained based on two challenging datasets which record both crowded scenes and non-crowded scenes.