Abstract
Videos have become an integral part of our life, from watching movies online to the use of videos in classroom teaching. Existing machine learning techniques are constrained with this scaled up activity because of this huge upsurge in online activity. A lot of research is now focused on reducing the time and accuracy of video classification. Content-Based Video Information Retrieval CBVIR implementation (E.g. Columbia374) is one such approach. We propose a fast Hamming Selection Pruned Sets (HSPS) algorithm that efficiently transforms multi-label video dataset into single-label representation. Thus, multi-label relationship between the labels can be retained for later single label classifier learning stage. Hamming distance (HD) is used to detect similarity between label-sets. HSPS captures new potential label-set relationships that were previously undetected by baseline approach. Experiments show a significant 22.9% dataset building time reduction and consistent accuracy improvement over the baseline method. HSPS also works on general multi-label dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Snoek, C.G.M., Worring, M.: Concept-Based Video Retrieval. Found. Trends Inf. Retr. 2, 215–322 (2009)
Bhatt, C., Kankanhalli, M.: Multimedia data mining: state of the art and challenges. Multimedia Tools and Applications 51, 35–76 (2011)
Yu-Gang, J., Jun, W., Shih-Fu, C., Chong-Wah, N.: Domain adaptive semantic diffusion for large scale context-based video annotation. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1420–1427 (2009)
Kennedy, L.S., Chang, S.-F.: A reranking approach for context-based concept fusion in video indexing and retrieval. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 333–340. ACM, Amsterdam (2007)
Wei, X.-Y., Jiang, Y.-G., Ngo, C.-W.: Exploring inter-concept relationship with context space for semantic video indexing. In: Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 1–8. ACM, Santorini (2009)
Yanagawa, A., Chang, S.F., Kennedy, L., Hsu, W.: Columbia university’s baseline detectors for 374 lscom semantic visual concepts. Columbia University ADVENT technical report 222-2006 (2007)
Yu-Gang, J., Jun, Y., Chong-Wah, N., Hauptmann, A.G.: Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study. IEEE Transactions on Multimedia 12, 42–53 (2010)
Snoek, C.G.M., Worring, M., van Gemert, J.C., Geusebroek, J.-M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 421–430. ACM, Santa Barbara (2006)
Jiang, Y.G., Yanagawa, A., Chang, S.F., Ngo, C.W.: CU-VIREO374: fusing Columbia374 and VIREO374 for large scale semantic concept detection 223, 1 (2008)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining Multi-label Data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, US (2010)
Hamming, R.W.: Error detecting and error correcting codes. Bell System Technical Journal 29, 147–160 (1950)
Read, J.: A pruned problem transformation method for multi-label classification, pp. 143–150 (2008)
Park, S.H., Fürnkranz, J.: Multi-label classification with label constraints, pp. 157–171 (2008)
Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems, vol. 14, pp. 681–687 (2001)
Xun, Y., Wei, L., Tao, M., Xian-Sheng, H., Xiu-Qing, W., Shipeng, L.: Automatic Video Genre Categorization using Hierarchical SVM. In: 2006 IEEE International Conference on Image Processing, pp. 2905–2908 (2006)
Nowak, E., Juries, F.: Vehicle categorization: parts for speed and accuracy. In: 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 277–283 (2005)
Petrovskiy, M.: Paired Comparisons Method for Solving Multi-Label Learning Problem. In: Sixth International Conference on Hybrid Intelligent Systems, HIS 2006, pp. 42–42 (2006)
Ráez, A.M., López, L.A.U., Steinberger, R.: Adaptive Selection of Base Classifiers in One-Against-All Learning for Large Multi-labeled Collections. In: Vicedo, J.L., Martínez-Barco, P., Muńoz, R., Saiz Noeda, M. (eds.) EsTAL 2004. LNCS (LNAI), vol. 3230, pp. 1–12. Springer, Heidelberg (2004)
Read, J., Bifet, A., Holmes, G., Pfahringer, B.: Efficient multi-label classification for evolving data streams (2010)
Tsoumakas, G., Vlahavas, I.P.: Random k-Labelsets: An Ensemble Method for Multilabel Classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406–417. Springer, Heidelberg (2007)
Cheng, W., Hüllermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Machine Learning 76, 211–225 (2009)
Platt, J.C.: 12 Fast Training of Support Vector Machines using Sequential Minimal Optimization (1998)
Van Gemert, J.C., Geusebroek, J., Veenman, C.J., Snoek, C.G.M., Smeulders, A.W.M.: Robust Scene Categorization by Learning Image Statistics in Context. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2006, pp. 105–105 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tang, T.Y., Alhashmi, S.M., Hisham Jaward, M. (2012). Hamming Selection Pruned Sets (HSPS) for Efficient Multi-label Video Classification. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-32695-0_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32694-3
Online ISBN: 978-3-642-32695-0
eBook Packages: Computer ScienceComputer Science (R0)