Xiaoshan Yang
ABSTRACT
The Bag-of-Words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to solve this problem. Nevertheless, these methods do not perform better than the state-of-the-art BOW based methods. In this paper, based on the advantages of BOW and Local NBNN, we introduce a novel locality discriminative coding (LDC) method. We convert each low level local feature, such as SIFT, into code vector using the Local Feature-to-Class distance other than by k-means quantization. Extensive experimental results on 4 challenging benchmark datasets show that our LDC method outperforms 6 state-of-the-art image classification methods (3 based on NBNN, 3 based on BOW).
- R. Behmo, P. Marcombes, A. Dalalyan, and V. Prinet. Towards optimal naive bayes nearest neighbor. In ECCV, 2010. Google Scholar
Digital Library
- O. Boiman, E. Shechtman, and M. Irani. In defense of nearest-neighbor based image classification. In CVPR, 2008.Google ScholarCross Ref
- G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In Workshop on ECCV, 2004.Google Scholar
- R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley Interscience, 2 edition, 2000. Google ScholarDigital Library
- R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. Liblinear: A library for large linear classification. Journal of Machine Learning Research. Google ScholarDigital Library
- L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In Workshop on CVPR, 2004. Google ScholarDigital Library
- Y. Huang, K. Huang, Y. Yu, and T. Tan. Salient coding for image classification. In CVPR, 2011.Google ScholarDigital Library
- S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006. Google ScholarDigital Library
- L.-J. Li and F.-F. Li. What, where and who? classifying events by scene and object recognition. In ICCV, 2007.Google ScholarCross Ref
- L. Liu, L. Wang, and X. Liu. In defense of soft-assignment coding. In ICCV, 2011.Google Scholar
- S. McCann and D. G. Lowe. Local naive bayes nearest neighbor for image classification. In CVPR, 2012. Google ScholarDigital Library
- M.-E. Nilsback and A. Zisserman. A visual vocabulary for flower classification. In CVPR, 2006. Google ScholarDigital Library
- J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In ICCV. Google ScholarDigital Library
- T. Tuytelaars, M. Fritz, K. Saenko, and T. Darrell. The nbnn kernel. In ICCV, 2011. Google ScholarDigital Library
- J. van Gemert, J.-M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders. Kernel codebooks for scene categorization. In ECCV (3), 2008. Google ScholarDigital Library
- J. Wang, J. Yang, K. Yu, F. Lv, T. S. Huang, and Y. Gong. Locality-constrained linear coding for image classification. In CVPR, 2010.Google ScholarCross Ref
- J. Yang, K. Yu, Y. Gong, and T. S. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR, 2009.Google Scholar
- B. Yao and L. Fei-Fei. Grouplet: A structured image representation for recognizing human and object interactions. In CVPR, San Francisco, USA, June 2010.Google ScholarCross Ref
Index Terms
- Locality discriminative coding for image classification
Recommendations
A new discriminative coding method for image classification
The bag-of-words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to ...
Class-specific image representation for image classification using multiple scale-invariant region detectors
We propose a new class-specific image representation for image classification using multiple region detectors. The new representation is designed to solve the problem of increasing variation in object location and size within images of a class, for ...
Early versus Late Dimensionality Reduction of Bag-of-Words Feature Representation for Image Classification
ICBRA '17: Proceedings of the 4th International Conference on Bioinformatics Research and ApplicationsExtracting the bag-of-words (BoW) feature from images has been widely used for image classification. In general, some local keypoints are first of all detected from each image and the keypoint descriptor, such as scale-invariant feature transform (SIFT),...
Comments