Abstract
Rapid growth of video content over internet has necessitated an immediate need to organize these large databases into meaningful categories. In this paper, we explore the benefits of leveraging social attitudes (beliefs, opinions, interests and evaluations of people) with machine learning concepts (audio/video features) in the challenging and pressing task of organization of online video databases. Through the analysis of view counts, we model social participation (people’s choices) towards a video’s contents. Observations reveal that viewership patterns are correlated with video genres. We propose logistic growth models to characterize videos based on usage and obtain a probability of video category. We then combine these subjectively assessed priors with likelihood of video class (as estimated from objective audio/video features) to establish the final category in a Bayesian framework. We provide a comparitive analysis of classification accuracies when a) categories are known a priori b) when they are not known a priori. Experimentally, we establish improvement in classification accuracy upon incorporating social attitudes with state-of-the-art audio/video features.
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
Brezeale, D., Cook, D.: Automatic Video Classification: A Survey of Literature. IEEE Trans. on Sys. Man and Cybernetics 38(3) (2008)
Chang, S., Ellis, D., Jiang, W., Lee, K., Yanagawa, A., Loui, A., Luo, J.: Large Scale Multimodal Semantic Concept Detection for Consumer Video. In: Intl. Workshop on Multimedia Information Retrieval (2007)
Crane, R., Sornette, D.: Viral, Quality and Junk Videos; Separating Content from Noise in an Information Rich Environment. In: AAAI Symposium on Social Information (2008)
Filippova, K., Hall, K.: Improved Video Categorization from Text Metadata and User Comments. In: Annual SIGIR Conference (2011)
Pentland, A.: Social Signal Processing. Signal Processing Magazine (2007)
Sargin, M., Aradhye, H.: Boosting Video Classification Using Cross-video Signals. In: ICASSP (2011)
Song, Y., Zhang, Y., Zhang, X., Cao, J., Li, J.: Google Challenge- Incremental Learning for Web Video Categorization on Robust semantic Feature Space. In: 17th ACM Conference on Multimedia (2009)
Song, Y., Zhao, M., Yagnik, J., Wu, X.: Taxonomic Classification of Web-based Videos. In: CVPR (2010)
Tsoularis, A.: Analysis of logistic growth models. Res. Lett. Inf. Math. Sci. (2001)
Wu, X., Zhao, W., Song, Y., Kumar, S., Li, B.: YouTubecat-Learning to Categorize Wild-web Videos. In: CVPR (2010)
Zhou, X., Huang, T.: Relevance Feedback in Image Retrieval: A Comprehensive Review. Multimedia Systems Special Issue on Content Based Image Retrieval (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Srinivasan, R., Roy-Chowdhury, A.K. (2013). Features with Feelings—Incorporating User Preferences in Video Categorization. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-37431-9_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37430-2
Online ISBN: 978-3-642-37431-9
eBook Packages: Computer ScienceComputer Science (R0)