Conference Papers
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Item Recent trends in content-based video copy detection(2010) Roopalakshmi, R.; Guddeti, G.R.M.Video copy detection is an interesting & challenging problem, as it is becoming increasingly important with the growing rate of illegal digital media and huge piracy issues. Therefore, in the present era of Internet &Multimedia technologies,the existence of ubiquitous digital videos, has led to the requirement of robust video copy detection techniques, for content management and copyright protection. In this paper, an overview of content based video copy detection (CBVCD) is presented along with the different state-of-the-art techniques used for video feature/ signature description. This article also explores the future key directions and highlights the research challenges that need to be addressed in the CBVCD paradigm. © 2010 IEEE.Item A novel approach to video copy detection using audio fingerprints and PCA(Elsevier B.V., 2011) Roopalakshmi, R.; Guddeti, G.R.M.In Content-Based Copy detection (CBCD) literature, numerous state-of-the-art techniques are primarily focusing on visual content of video. Exploiting audio fingerprints for CBCD problem is necessary, because of following rea-sons: audio content constitutes an indispensable information source; transformations on audio content is limited compared to visual content. In this paper, a novel CBCD approach using audio features and PCA is proposed, which includes two stages: first, multiple feature vectors are computed by utilizing MFCC and four spectral descriptors; second, features are further processed using PCA, to provide compact feature description. The results of experiments tested on TRECVID-2007 dataset, demonstrate the efficiency of proposed method against various transformations. © 2011 Published by Elsevier Ltd.Item Efficient video copy detection using simple and effective extraction of color features(2011) Roopalakshmi, R.; Guddeti, G.R.M.In the present Multimedia era, the exponential growth of illegal videos and huge piracy issues increased the importance of Content Based video Copy Detection (CBCD) techniques. CBCD systems require compact and computationally efficient descriptors for detecting video copies. In this paper, we propose a simple and efficient video signature scheme using Dominant Color Descriptors of MPEG-7 standard in order to implement the proposed CBCD task. Experimental results show that the proposed approach yields better detection rates when compared to that of existing approaches, against common transformations like Contrast change, Noise addition, Rotation, Zooming, Blurring etc. Further, evaluation results also prove that our scheme is computationally efficient by supporting substantial reduction in the total computational cost up to the extent of 65% when compared to that of existing schemes. © 2011 Springer-Verlag.Item Compact and efficient CBCD scheme based on integrated color features(2011) Roopalakshmi, R.; Guddeti, G.R.M.Content-Based Copy Detection (CBCD) is an emergent research topic and has been extensively studied recently. Color is one of the most important and easily recognizable features of visual content. To address efficiency and effectiveness issues of CBCD systems, in this article a compact and computationally inexpensive CBCD scheme using MPEG-7 Dominant Color Descriptor is proposed. This paper proposes a novel dominant color extraction technique, which solves the intrinsic problems of existing color clustering techniques. Experimental results show that the proposed scheme improves detection accuracy up to 38%, and also supports significant reduction in total computational cost up to the extent of 91%, when compared with that of existing schemes against various video transformations. © 2011 IEEE.Item A novel CBCD approach using MPEG-7 motion activity descriptors(2011) Roopalakshmi, R.; Guddeti, G.Motion features contribute significant information about a video content. This paper highlights a novel CBCD (Content-Based Copy Detection) approach, by incorporating several motion activity features. First, we extract both temporal and spatial motion features to describe overall activity of a video sequence. Second, we combine these features in a feasible manner, to generate robust video fingerprints. Third, clustering based pruned search is utilized for similarity matching instead of direct searching of video fingerprints. The proposed system is tested on TRECVID-2007 data set and the results demonstrate the effectiveness of the proposed system against several transformations such as random noise, fast forward, pattern insertion, cropping and picture-inside-picture. © 2011 IEEE.Item Towards a new approach to video copy detection using acoustic features(2011) Roopalakshmi, R.; Guddeti, G.Acoustic features are robust and powerful in video description, but not fully exploited for the emerging Content-Based video Copy Detection (CBCD) methods. To solve this discrepancy, this paper proposes a new CBCD approach using audio spectral features compared to existing visual content based methods. The proposed method incorporates three stages: 1) Extraction of spectral descriptors including centroid and energy; 2) Integration of resultant features to compute highly informative spectral descriptive words; 3) Utilization of clustering approach to speed up the similarity matching process. The results tested on TRECVID-2008 dataset, demonstrate the improved detection accuracy of proposed method (up to 27.845%) compared to reference methods against various transformations such as fast forward, slow motion, mp3 compression, and multiband companding. © 2011 IEEE.Item Robust features for accurate spatio-temporal registration of video copies(2012) Roopalakshmi, R.; Guddeti, G.Pirate copies of movies are proliferating on the Internet and causing huge piracy issues. Any anti-piracy strategy requires not only copy detection but also precise frame alignment of pirate video with master content, prior to the estimation of geometric distortions and capture location in a theater. Most studies in pirate video registration focus on the alignment of watermarked sequences, while few efforts are made to align non-watermarked videos using content-based features. In this paper, we propose a spatio-temporal scheme for aligning pirate and master contents using visual features, which consists of three stages: First, a video sequence is compactly represented using 1-D SURF (Speeded-Up Robust Features) signatures; Second, temporal frame alignments are computed using sliding window based dynamic programming method; Third, robust SURF descriptors are employed to generate spatial frame alignments. The results demonstrate the improved registration accuracy of the proposed method against various transformations. © 2012 IEEE.Item Content-Based Video Copy Detection scheme using motion activity and acoustic features(Springer Verlag service@springer.de, 2014) Roopalakshmi, R.; Guddeti, G.R.M.This paper proposes a new Content-Based video Copy Detection (CBCD) framework, which employs two distinct features namely, motion activity and audio spectral descriptors for detecting video copies, when compared to the conventional uni-feature oriented methods. This article focuses mainly on the extraction and integration of robust fingerprints due to their critical role in detection performance. To achieve robust detection, the proposed framework integrates four stages: 1) Computing motion activity and spectral descriptive words; 2) Generating compact video fingerprints using clustering technique; 3) Performing pruned similarity search to speed up the matching task; 4) Fusing the resultant similarity scores to obtain the final detection results. Experiments on TRECVID-2009 dataset demonstrate that, the proposed method improves the detection accuracy by 33.79% compared to the referencemethods. The results also prove the robustness of the proposed framework against different transformations such as fast forward, noise, cropping, picture-inpicture and mp3 compression. © Springer International Publishing Switzerland 2014.