Learning Convolutional NonLinear Features for K Nearest Neighbor Image Classification | |
Weiqiang Ren; Yinan Yu; Junge Zhang; Kaiqi Huang | |
2014 | |
会议名称 | International Conference on Pattern Recognition |
会议录名称 | Proc. International Conference on Pattern Recognition 2014 |
页码 | 2938-2943 |
会议日期 | 2014-08-01 |
会议地点 | Stockholm, Sweden |
摘要 | Learning 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. |
关键词 | Computer Vision convolution image Classification |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12685 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Kaiqi Huang |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 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. |
条目包含的文件 | 条目无相关文件。 |
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