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	Topic level sampling towards optimized locality sensitive vocabulary coding
	Chen, Jie ; Duan, Ling-Yu ; Li, Bing ; Ji, Rongrong ; Gao, Wen
	2011
英文摘要	We propose a novel locality sensitive vocabulary coding scheme to extract compact descriptors for low bit rate visual search. We employ Latent Dirichlet Allocation (LDA) to learn the topic vocabularies of lower dimension to generate compact descriptors. To deal with diverse datasets, LDA model is introduced to subdivide a dataset into groups of images with a topic model, where the code word distributions in each group produce coherent statistics from generative learning. Moreover, our empirical study has shown that the original Bag-of-Word (BoW) is sparse, and the occurrences of non-zero words is coherent within a topic. Our proposed topic-wise vocabulary learning yields a more compact yet discriminative codebook to search images. Given a query image, multiple topics are determined, which is fed into the topic vocabularies to generate more compact topical descriptor. Comparison experiments show our topic-wise locality sensitive vocabulary coding produces more compact and discriminative descriptors than the state-of-the-arts. ? 2011 IEEE.; EI; 0
语种	英语
DOI标识	10.1109/ICICS.2011.6174287
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/328613]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Chen, Jie,Duan, Ling-Yu,Li, Bing,et al. Topic level sampling towards optimized locality sensitive vocabulary coding. 2011-01-01.

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