A novel reduced category specific SIFT descriptor based on affinity propagation for CBIR

Yan Zhang; Xiao Han; Zhaoxing Zhou; Yong Fu

doi:10.1117/12.833968

30 October 2009 A novel reduced category specific SIFT descriptor based on affinity propagation for CBIR

Yan Zhang, Xiao Han, Zhaoxing Zhou, Yong Fu

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74962O (2009) https://doi.org/10.1117/12.833968
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

We proposed a novel method to extract and reduce Category Specific SIFT Descriptor (CSSD). Our approach is based on two facts. One is that in many images there are always more than two different objects and this brings on ambiguity of the categories which they should belong to. The other is that the number of SIFT features for one image often varies from tens to thousands, matching these SIFT features of two arbitrary images brings high computational costs. As for the first fact, those category specific SIFT features hide among the sum of SIFT information, we aim to filter out the contributive SFIT information to category recognition by clustering all. With respect to the second fact, the SIFT clusters can instruct to reduce each image SIFT features by keeping high occurrence frequency ones. So the more precious and smaller SIFT features depending on its category specific features can be obtained. Another main highlight of our approach is the sensible use of affinity propagation to address the definition of clustering category K more objectively. Extensive experiments shows that the RCSSD (Reduced CSSD) obtained by affinity propagation clustering outperforms the original SIFT descriptor and RCSSD by using K-means approach.

Citation Download Citation

Yan Zhang, Xiao Han, Zhaoxing Zhou, and Yong Fu "A novel reduced category specific SIFT descriptor based on affinity propagation for CBIR", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74962O (30 October 2009); https://doi.org/10.1117/12.833968

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KEYWORDS

Feature extraction

Image retrieval

Databases

Buildings

Classification systems

Data centers

Content based image retrieval

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