Approach we have tested in our submitted runs: For visual- based copy detection, we find links between video shot key-frames using a probabilistic latent space model over local matches between the keyframe images. This facilitates the extraction of significant groups of local matching descriptors that may represent common semantic elements of near duplicate key-frames. For 2009, we have worked on an optimal representation of the test database. We first select the discriminant local descriptors. Then, we quantize the selected local descriptors into a hierarchical structure. For audio based copy detection, we give results with two different feature parameters: 15-bit energy difference parameters similar to [1] and a feature-based mapping of test frames to query frames. Differences we found among the runs: We submitted 1 run for the video only copy detection task (same run for Balanced and for

We present a fuzzy color histogram-based shot-boundary detection algorithm specialized for content based copy detection applications. The proposed method aims to detect both cuts and gradual transitions (fade, dissolve) effectively in videos where heavy transformations (such as cam-cording, insertions of patterns, strong re-encoding) occur. Along with the color histogram generated with the fuzzy linking method on L*a*b* color space, the system extracts a mask for still regions and the window of picture-in-picture transformation for each detected shot, which will be useful in a content-based copy detection system. Experimental results show that our method effectively detects shot boundaries and reduces false alarms as compared to the state-of-the-art shot-boundary detection algorithms.

c t i v it y e p o r t 2008 Table of contents

We propose a video copy detection framework that detects copy segments by fusing the results of three different techniques: facial shot matching, activity subsequence matching, and non-facial shot matching using low-level features. In facial shot matching part, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In activity subsequence matching part, a spatio-temporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Lastly, the non-facial shots are matched using low-level MPEG-7 descriptors and dynamic-weighted feature similarity calculation. The proposed framework is tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results are compared with the results of top-8 most successful techniques submitted to this task. Promising results are obtained in terms of both effectiveness and efficiency

Inthispaper,wefocusonanewapplicationofmobilevisualsearch: snapping a photo with a mobile device of a video playing on a TV screen to automatically retrieve and stream the remainder of the video to the mobile device. When the user takes a photo of the video,thecapturedqueryframemaycontaintoofewusefulfeatures for good retrieval performance. We design and implement a new algorithm for mobile video retrieval to accurately select a featurerich frame from a sequence of viewfinder frames in a very short temporal window determined by the user-initiated query event. Fast and accurate selection using efficiently computed Hessian scores is developed for real-time operation on mobile devices. Viewfinder frames captured before the query starts are pre-processed, while the number of viewfinder frames captured afterwards is minimized by a probabilistic optimization process. Evaluated on a large video database of 10 million frames, dynamic query frame selection provides a substantial increase in retrieval accuracy with very low search latency.

journal homepage: www.elsevier.com/locate/cviu

c t i v it y e p o r t 2008 Table of contents

*: officially submitted run. This paper describes the TRECVID 2011 participation of the IUPR-DFKI team in the semantic indexing task (SIN) and content based copy detection task (CCD) task. For SIN, this years participation was dominated by an significant increase of vocabulary concept size from 130 to 346 concepts. In particular the system setup has been changed to last year’s participation [6] with respect to computational demands employing less computational costly features for classification and no usage of external training sources like YouTube. For CCD, this years participation is aimed at testing the flip invariant SIFT applied in video-only CCD. At the same time, we investigated how well we could achieve by relying on one keypoint feature alone

Content-Based Video Copy Detection (CBVCD) consists of detecting and retrieving videos that are copies of known original videos. CBVCD systems rely on two different tasks: Feature Extraction task, that calculates many representative descriptors for a video sequence, and Similarity Search task, that is the algorithm for finding videos in an indexed collection that match a query video. This paper describes P-VCD, which is a novel approach for CBVCD based on global descriptors, weighted combinations of distances, a pivot-based index structure, an approximate similarity search, and a voting algorithm for copy localization. P-VCD was tested at the TRECVID 2010 evaluation, where it was the best positioned CBVCD system for Balanced and No False Alarms profiles considering visual-only runs (and above the median considering all runs). P-VCD shows that by using approximate similarity searches one can obtain good effectiveness, and that global descriptors can achieve competitive results with TRECVID transformations.

Content-Based Multimedia Information Retrieval retrieves multimedia documents based on their content (colors, edges, textures, etc.). The content of a whole multimedia document is represented by a global descriptor. The similarity of two multimedia documents can be defined as the distance between their descriptors. A multi-metric function that combines distances from many descriptors usually outperforms the effectiveness of any single descriptor. In this case, a different weight is assigned to each descriptor representing its relative importance in the combination. Usually, these sets of weights are fixed manually or by performing many effectiveness evaluations. In this work, we present three novel techniques for weighting multi-metrics: α-normalization, which is a generalization of the normalization