Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 62, Issue 1, Pages 564-571Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2014.2327558
Keywords
Cellets; fine grained; hierarchical; image categorization; spatial pyramid
Categories
Funding
- National Natural Science Foundation of China [61125106, 61327902, 61035002, 61373076, 61002009]
- Chinese Academy of Sciences [KGZD-EW-T03]
- Fundamental Research Funds for the Central Universities [2013121026]
- Key Science and Technology Program of Zhejiang Province of China [2012C01035]
- Zhejiang Provincial Natural Science Foundation of China [LZ13F020004]
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In this paper, a new fine-grained image categorization system that improves spatial pyramid matching is developed. In this method, multiple cells are combined into cellets in the proposed categorization model, which are highly responsive to an object's fine categories. The object components are represented by cellets that can connect spatially adjacent cells within the same pyramid level. Here, image categorization can be formulated as the matching between the cellets of corresponding images. Toward an effective matching process, an active learning algorithm that can effectively select a few representative cells for constructing the cellets is adopted. A linear-discriminant-analysis-like scheme is employed to select discriminative cellets. Then, fine-grained image categorization can be conducted with a trained linear support vector machine. Experimental results on three real-world data sets demonstrate that our proposed system outperforms the state of the art.
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