Computer Science and Information Systems 2011 Volume 8, Issue 3, Pages: 931-951
https://doi.org/10.2298/CSIS100423035J
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Multi-scale image semantic recognition with hierarchical visual vocabulary
Jiang Xinghao (School of Information Security Engineering Shanghai Jiao Tong University, Shanghai, China + Key Lab. of Shanghai Information Security Management and Technology Research, Shanghai, China)
Sun Tanfeng (School of Information Security Engineering Shanghai Jiao Tong University, Shanghai, China + Key Lab. of Shanghai Information Security Management and Technology Research, Shanghai, China)
Guanglei Fu (School of Information Security Engineering Shanghai Jiao Tong University, Shanghai, China)
Local features have been proved to be effective in image/video semantic
analysis. The BOVW (bag of visual words) scheme can cluster local features to
form the visual vocabulary which includes an amount of words, where each word
is the center of one clustering feature. The vocabulary is used to recognize
the image semantic. In this paper, a new scheme to construct semantic-binding
hierarchical visual vocabulary is proposed. Some attributes and relationship
of the semantic nodes in the model are discussed. The hierarchical semantic
model is used to organize the multi-scale semantic into a level-by-level
structure. Experiments are performed based on the LabelMe dataset, the
performance of our scheme is evaluated and compared with the traditional BOVW
scheme, experimental results demonstrate the efficiency and flexibility of
our scheme.
Keywords: local feature, bag of visual words, image semantic analysis, visual vocabulary