Abstract
We propose a method for classification of pitch frames in a cricket video based on bag-of-visual-words technique. Bag of visual words is a popular and successful technique in image classification and object-based classifications. In this paper, we demonstrate three different techniques based on bag-of-words methodology. The three different techniques use three different set of features for the classification of pitch frames in a cricket video. The three different types of features we use are SIFT (Scale-Invariant Feature Transform), LBP (Local Binary Patterns), and CTE (Color+Texture+Edge) features. We evaluate the three techniques on the dataset of cricket (http://cse.iitk.ac.in/~vision/dipen/), made available online by Mr. Dipen Raghuwani. Our experiments by using the above mentioned three types of features have shown significant results.
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References
Video dataset. http://cse.iitk.ac.in/~vision/dipen/.
Jayanth SB, Srinivasa G. Automated classification of cricket pitch frames in cricket video. Electron Lett Comput Vis Image Anal. 2014;13(1):33–49.
Kolekar MH, Palaniappan K, Sengupta S. Semantic event detection and classification in cricket video sequence. In: Sixth Indian Conference on Computer Vision, Graphics and Image Processing; 2008.
Wang X, Wang LM, Qiao Y. A comparative study of encoding, pooling and normalization methods for action. Recognition ACCV 2012, Part III, LNCS. 2013;7726:572–85.
Tsai C-F. Bag-of-words representation in image annotation: a review. In: International scholarly research network, ISRN artificial intelligence; 2012.
O’hara S, Draper BA. Introduction to the bag of features paradigm for image classification and retrieval. arXiv:1101.3354v1 [cs.CV] 17 Jan 2011.
Grauman K, Leibe B, Chapter 5. Indexing and visual vocabularies. Excerpt chapter from synthesis lecture draft: visual recognition.
Ojala T, Pietikainen M, Maenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 2002;24(7):971–87.
Lin CY, Wu M, Bloom JA, Cox IJ, Miller M. Rotation, scale, and translation resilient public watermarking for images. IEEE Trans Image Process. 2001;10(5):767–82.
Vedaldi A, Fulkerson B. VLFeat: an open and portable library of computer vision algorithms; 2008. http://www.vlfeat.org/.
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Ravinder, M., Venugopal, T. (2016). Pitch Frames Classification in a Cricket Video Using Bag-of-Visual-Words. In: Dash, S., Bhaskar, M., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 394. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2656-7_72
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DOI: https://doi.org/10.1007/978-81-322-2656-7_72
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