Shot boundary detection using color correlogram and Gauge-SURF descriptors

Automatic shot boundary detection has been identified to be one of the important and challenging research areas. Shot boundary detection is mainly helpful in video summarization, video indexing and content based video retrieval systems. In this paper we propose an efficient algorithm to detect shot boundaries using color correlogram and Gauge-Speeded-up robust features. Proposed algorithm analyses the changes in color correlogram and the correspondence between Gauge-Speeded-up robust features over selected frames of a video to find the shot boundaries. Our proposed methodology can detect both abrupt and gradual shot boundaries in videos efficiently. Experimental results clearly prove the effectiveness of our proposed method in detecting shot boundaries.

[1]  Frank S. Marzano,et al.  Iterative Bayesian Retrieval of Hydrometeor Content From X-Band Polarimetric Weather Radar , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Sarah V. Porter,et al.  Video Segmentation and Indexing using Motion Estimation , 2004 .

[3]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[4]  Xiangyang Xue,et al.  Shot boundary detection using unsupervised clustering and hypothesis testing , 2004, 2004 International Conference on Communications, Circuits and Systems (IEEE Cat. No.04EX914).

[5]  Steven C. H. Hoi,et al.  Chinese University of Hong Kong at TRECVID 2006: Shot Boundary Detection and Video Search , 2006, TRECVID.

[6]  Xinbo Gao,et al.  A Video Shot Boundary Detection Algorithm Based on Feature Tracking , 2006, RSKT.

[7]  G. Camara-Chavez,et al.  Shot Boundary Detection by a Hierarchical Supervised Approach , 2007, 2007 14th International Workshop on Systems, Signals and Image Processing and 6th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services.

[8]  Pavel Zemcík,et al.  TRECVID 2007 by the Brno Group , 2007, TRECVID.

[9]  Pong C. Yuen,et al.  Shot Boundary Detection: An Information Saliency Approach , 2008, 2008 Congress on Image and Signal Processing.

[10]  Ki Tae Park,et al.  Object-Based Image Retrieval Using Dominant Color Pairs Between Adjacent Regions , 2006, ICCSA.

[11]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Marcin Grzegorzek,et al.  Shot boundary detection using spectral clustering , 2007, 2007 15th European Signal Processing Conference.

[13]  R DeepakCRDeepakC.  A Novel Approach for Query by Video Clip , 2013 .

[14]  Antoni B. Chan,et al.  Time Series Models for Semantic Music Annotation , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[15]  Frédéric Precioso,et al.  Shot Boundary Detection at TRECVID 2006 , 2006, TRECVID.

[16]  Gokul,et al.  Content Based Video Retrieval Using Cluster Overlapping , 2013 .

[17]  Zhi-Cheng Zhao,et al.  Shot Boundary Detection Algorithm in Compressed Domain Based on Adaboost and Fuzzy Theory , 2006, ICNC.

[18]  Luis Miguel Bergasa,et al.  Gauge-SURF descriptors , 2013, Image Vis. Comput..

[19]  Chi-Chun Lo,et al.  Video segmentation using a histogram-based fuzzy c-means clustering algorithm , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[20]  Chen Xufeng,et al.  Image retrieval based on optimal matching with block histogram , 2010, The 2nd International Conference on Information Science and Engineering.