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Green Image
Issue No. 03 - March (2014 vol. 36)
ISSN: 0162-8828
pp: 417-435
Lei Wang , Sch. of Comput. Sci. & Software Eng., Univ. of Wollongong, Wollongong, NSW, Australia
Luping Zhou , Sch. of Comput. Sci. & Software Eng., Univ. of Wollongong, Wollongong, NSW, Australia
Chunhua Shen , Sch. of Comput. Sci., Univ. of Adelaide, Adelaide, SA, Australia
Lingqiao Liu , Sch. of Comput. Sci. & Software Eng., Univ. of Wollongong, Wollongong, NSW, Australia
Huan Liu , Sch. of Comput., Inf., & Decision Syst. Eng., Arizona State Univ., Tempe, AZ, USA
In image recognition with the bag-of-features model, a small-sized visual codebook is usually preferred to obtain a low-dimensional histogram representation and high computational efficiency. Such a visual codebook has to be discriminative enough to achieve excellent recognition performance. To create a compact and discriminative codebook, in this paper we propose to merge the visual words in a large-sized initial codebook by maximally preserving class separability. We first show that this results in a difficult optimization problem. To deal with this situation, we devise a suboptimal but very efficient hierarchical word-merging algorithm, which optimally merges two words at each level of the hierarchy. By exploiting the characteristics of the class separability measure and designing a novel indexing structure, the proposed algorithm can hierarchically merge 10,000 visual words down to two words in merely 90 seconds. Also, to show the properties of the proposed algorithm and reveal its advantages, we conduct detailed theoretical analysis to compare it with another hierarchical word-merging algorithm that maximally preserves mutual information, obtaining interesting findings. Experimental studies are conducted to verify the effectiveness of the proposed algorithm on multiple benchmark data sets. As shown, it can efficiently produce more compact and discriminative codebooks than the state-of-the-art hierarchical word-merging algorithms, especially when the size of the codebook is significantly reduced.
Visualization, Algorithm design and analysis, Merging, Tin, Histograms, Training, Computational modeling,object recognition, Hierarchical word merge, compact codebook, class separability, bag-of-features model
Lei Wang, Luping Zhou, Chunhua Shen, Lingqiao Liu, Huan Liu, "A Hierarchical Word-Merging Algorithm with Class Separability Measure", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 36, no. , pp. 417-435, March 2014, doi:10.1109/TPAMI.2013.160
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