Image classification using boosted local features with random orientation and location selection
文献类型：期刊论文
| 作者 | Zhang CJ(张淳杰) ; Cheng J(程健); Zhang YF(张一帆); Liu J(刘静); Liang C(梁超); Pang JB(庞俊彪); Huang QM(黄庆明); Tian Q(田奇)
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| 刊名 | Information Sciences
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| 出版日期 | 2015 |
| 期号 | 310页码:118-129 |
| 关键词 | Sparse Coding Image Classification Random Orientation Boosting Local Feature Selection |
| 英文摘要 | The combination of local features with sparse technique has improved image classification performance dramatically in recent years. Although very effective, this strategy still has two shortcomings. First, local features are often extracted in a pre-defined way (e.g. SIFT with dense sampling) without considering the classification task. Second, the codebook is generated by sparse coding or its variants by minimizing the reconstruction error which has no direct relationships with the classification process. To alleviate the two problems, we propose a novel boosted local features method with random orientation and location selection. We first extract local features with random orientation and location using a weighting strategy. This randomization process makes us to extract more types of information for image representation than pre-defined methods. These extracted local features are then encoded by sparse representation. Instead of generating the codebook in a single process, we construct a series of codebooks and the corresponding encoding parameters of local features using a boosting strategy. The weights of local features are determined by the classification performances of learned classifiers. In this way, we are able to combine the local feature extraction and encoding with classifier training into a unified framework and gradually improve the image classification performance. Experiments on several public image datasets prove the effectiveness and efficiency of the proposed method. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/15381]  |
| 专题 | 类脑芯片与系统研究 |
| 作者单位 | 1.School of Computer and Control Engineering, University of Chinese Academy of Sciences 2.Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences 3.National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences 4.National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University 5.Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology 6.Key Lab of Intell. Info. Process, Institute of Computing Technology, Chinese Academy of Sciences 7.Department of Computer Sciences, University of Texas at San Antonio |
| 推荐引用方式 GB/T 7714 | Zhang CJ,Cheng J,Zhang YF,et al. Image classification using boosted local features with random orientation and location selection[J]. Information Sciences,2015(310):118-129. |
| APA | Zhang CJ.,Cheng J.,Zhang YF.,Liu J.,Liang C.,...&Tian Q.(2015).Image classification using boosted local features with random orientation and location selection.Information Sciences(310),118-129. |
| MLA | Zhang CJ,et al."Image classification using boosted local features with random orientation and location selection".Information Sciences .310(2015):118-129. |
入库方式： OAI收割
来源：自动化研究所
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