Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

VIT Unitversity

Department of Multimedia

School of Design

Vellore Campus

2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN)

Multimedia Indexing and Retrieval: Recent research work and their challenges

Video shot boundary detection (SBD) is the first step of video analysis, summarization, indexing, and retrieval. In SBD process, videos are segmented into basic units called shots. In this paper, a new SBD method is proposed using color, edge, texture, and motion strength as vector of features (feature vector). Features are extracted by projecting the frames on selected basis vectors of Walsh-Hadamard transform (WHT) kernel and WHT matrix. After extracting the features, based on the significance of the features, weights are calculated. The weighted features are combined to form a single continuity signal, used as input for Procedure Based shot transition Identification process (PBI). Using the procedure, shot transitions are classified into abrupt and gradual transitions. Experimental results are examined using large-scale test sets provided by the TRECVID 2007, which has evaluated hard cut and gradual transition detection. To evaluate the robustness of the proposed method, the system evaluation is performed. The proposed method yields F1-Score of 97.4% for cut, 78% for gradual, and 96.1% for overall transitions. We have also evaluated the proposed feature vector with support vector machine classifier. The results show that WHT-based features can perform well than the other existing methods. In addition to this, few more video sequences are taken from the Openvideo project and the performance of the proposed method is compared with the recent existing SBD method. © 2014 IEEE.

World University root node

IEEE Transactions on Image Processing

Walsh–Hadamard Transform Kernel-Based Feature Vector for Shot Boundary Detection

Among possible research area in multimedia, keyframe extraction is an important topic that provides video summarization, faster browsing and accessing of wide video collections. In this paper, we propose a new automatic shot based keyframe extraction for video indexing and retrieval applications. Initially, the frames are sequentially clustered into shots by using feature extraction, continuity value construction steps of shot boundary detection process and the shot frame clustering technique. The cluster having a larger dispersion rate is selected for inter cluster similarity analysis (ICSA) and the sub-shot based keyframes are extracted using ICSA. The proposed shot boundary detection algorithm and video keyframe extraction technique are implemented and evaluated on publicly available ecological video datasets. Compared with existing related algorithms, our method yields better F1-score of 94.2% for shot boundary detection and better results for keyframe extraction. The keyframes extracted by the proposed method are used for video indexing and retrieval. © 2013 Elsevier B.V.

Ecological Informatics

Shot based keyframe extraction for ecological video indexing and retrieval

Shot transition detection is a basic step in video content analysis and retrieval. A number of automatic detection techniques exist but they leak their performance in the detection of gradual transitions. These techniques often suffer from high false detection rates due to the presence of illumination, camera or object movements in the video sequences. In order to reduce the influences of these effects in the detection process and to detect long dissolve and fade regions in the video sequences, we propose a modified color layout descriptor with a new similarity metric; suits well for the extracted features to identify and classify the gradual transitions. Experiments are carried out on various video sequences taken from TRECVid dataset and publically available dataset. The results show that the proposed method yields better result compared to that of the existing gradual transition detection methods. © 2012 Springer-Verlag.

Communications in Computer and Information Science

Modified color layout descriptor for gradual transition detection

Video shot boundary detection is the basic step in the area of content based video analysis and retrieval. Various automatic shot boundary detection techniques have been proposed and their performances are reliable, especially for video cut detection. However most of the proposed methods are sensitive to camera, object motion and lighting changes. In this paper, we have focused mainly on the improvement of the existing traditional shot detection methods. In our technique, approximation of the discontinuity values obtained from the various existing methods has been done using the Least Square polynomial approximation method to diminish the sensitivity to motion. An automatic threshold calculation algorithm is also used in our method. Experimental results demonstrate that our method can detect maximum shot breaks and reduces the occurrence of false hit that are difficult with the previous approaches when motion occurs in the video. © Springer-Verlag 2011.

Video shot cut detection using least square approximation method

Shot boundary detection is a field of research of video processing. It aims to automatically detect the transition between the shots in the digital video. In this paper, a new video cut transition detection algorithm is presented by using Hilbert transform and Gray level co-occurrence matrix (GLCM). Texture features are extracted from the GLCM and the corresponding distance measure between the adjacent frames are calculated. Peak values among the distance measures are identified and compared with the threshold. The frame is declared as cut frame if the peak value is greater than that of the calculated threshold. Experimental results shows that our method provides better results when compared to previous existing methods. © 2011 IEEE.

International Conference on Recent Trends in Information Technology, ICRTIT 2011

Video cut detection using Hilbert transform and GLCM

Video shot boundary detection has been an area of active research in recent years. It plays major role in digital video analysis domain: video compression, video indexing, video content based retrieval, video scene detection and video object tracking. This paper approaches the video cut transition detection based on the block wise histogram differences of the dominant color features in the HSV color space. Most of the cut identification techniques uses a thresholding operation to discriminate between the inter frame difference metrics values and thus identify the video breakpoints. An automatic threshold calculation algorithm is used for cut identification process. Experimental results show that the proposed method gives better results than the existing methods. © 2010 ACM.

ACM International Conference Proceeding Series

Video cut detection using dominant color features

Shot boundary detection algorithm has the longest and richest history in the area of content based video analysis and retrieval. Various automatic shot boundary detection techniques have been proposed and their performances are reliable, especially for video cut detection. This paper approaches the video cut transition detection based on the block wise histogram differences of the two consecutive frames of a video sequence in RGB color space. Most of the cut identification techniques uses a threshold to discriminate between the inter frame difference values and thus identify the video breakpoints. A new threshold calculation algorithm is proposed for cut identification process. Experimental results show that the proposed method gives better results than the existing methods. ©2010 IEEE.