Xian-hua Han
ABSTRACT
This paper develops a supervised nonlinear subspace of bag-of-features for category classification. Bag-of-features represents an image as an orderless distribution of features, which selects the visual words by clustering and uses the similarity with each visual word as the features for classification. In this paper, we propose to model the ensemble of visual words with a supervised nonlinear neighborhood embedding method to a more discriminative space for category classification. The supervised nonlinear neighborhood embedding(SNNE) is used to model visual words and extract the discrimitive information specialized for each category. The projection length in subspace is used as features for classification. The SNNE subspace method can model the nonlinear variations induced by various kinds of visual words and extract more discriminative feature for object recognition. The proposed method is evaluated using the Cal-tech and GRAZ01 database. We confirm that the proposed method is comparable with state-of-the-art methods without absolute position information.
- D. Lowe, "Distinctive image features from scale-invariant keypoints", International Journal of Computer Vision, 60(2), pp. 91--110, 2004. Google Scholar
Digital Library
- G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, "Visual categoraization with bags of keypoints", In Proc. ECCVWorkshop on Statistical Learning in Computer Vision, pp. 1--16, 2004.Google Scholar
- R. Fergus, P. Perona, and A. Zisserman, "Object class recognition by unsupervised scale-invariant learning", In Proc. CVPR, 2003.Google ScholarCross Ref
- Xian-Hua Han, Yen-Wei Chen, Takeshi Sukegawa, "A supervised nonlinear neighborhood embedding of color histogram for image indexing", ICIP, pp. 949--952, 2008.Google Scholar
- A. Opelt, M. Fussenegger, A. Pinz, and P. Auer. Weak hypotheses and boosting for generic object detection and recognition. In Proc. ECCV, volume 2, pp. 71--84, 2004.Google ScholarCross Ref
- Lazebnik S., Schmid C., Ponce J, "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories", In Proc. CVPR, pp. 2169--2178, 2006 Google ScholarDigital Library
Index Terms
- Object class recognition with supervised nonlinear neighborhood embedding of visual words
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