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Learning Convolutional NonLinear Features for K Nearest Neighbor Image Classification
Weiqiang Ren; Yinan Yu; Junge Zhang; Kaiqi Huang
Conference NameInternational Conference on Pattern Recognition
Source PublicationProc. International Conference on Pattern Recognition 2014
Conference Date2014-08-01
Conference PlaceStockholm, Sweden
AbstractLearning low-dimensional feature representations is a crucial task in machine learning and computer vision. Recently the impressive breakthrough in general object recognition made by large scale convolutional networks shows that convolutional networks are able to extract discriminative hierarchical features in large scale object classification task. However, for vision tasks other than end-to-end classification, such as K Nearest Neighbor classification, the learned intermediate features are not necessary optimal for the specific problem. In this paper, we aim to exploit the power of deep convolutional networks and optimize the output feature layer with respect to the task of K Nearest Neighbor (kNN) classification. By directly optimizing the kNN classification error on training data, we in fact learn convolutional nonlinear features in a data-driven and task-driven way. Experimental results on standard image classification benchmarks show that the proposed method is able to learn better feature representations than other general end-to-end classification methods on kNN classification task.
KeywordComputer Vision   convolution   image Classification
Document Type会议论文
Corresponding AuthorKaiqi Huang
Recommended Citation
GB/T 7714
Weiqiang Ren,Yinan Yu,Junge Zhang,et al. Learning Convolutional NonLinear Features for K Nearest Neighbor Image Classification[C],2014:2938-2943.
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