Topical Video Object Discovery from Key Frames by Modeling Word Co-occurrence Prior

Gangqiang Zhao, Junsong Yuan, Gang Hua; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 1602-1609

Abstract


A topical video object refers to an object that is frequently highlighted in a video. It could be, e.g., the product logo and the leading actor/actress in a TV commercial. We propose a topic model that incorporates a word co-occurrence prior for efficient discovery of topical video objects from a set of key frames. Previous work using topic models, such as Latent Dirichelet Allocation (LDA), for video object discovery often takes a bag-of-visual-words representation, which ignored important co-occurrence information among the local features. We show that such data driven co-occurrence information from bottom-up can conveniently be incorporated in LDA with a Gaussian Markov prior, which combines top down probabilistic topic modeling with bottom up priors in a unified model. Our experiments on challenging videos demonstrate that the proposed approach can discover different types of topical objects despite variations in scale, view-point, color and lighting changes, or even partial occlusions. The efficacy of the co-occurrence prior is clearly demonstrated when comparing with topic models without such priors.

Related Material


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[bibtex]
@InProceedings{Zhao_2013_CVPR,
author = {Zhao, Gangqiang and Yuan, Junsong and Hua, Gang},
title = {Topical Video Object Discovery from Key Frames by Modeling Word Co-occurrence Prior},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}