Deep sparse representation-based mid-level visual elements discovery in fine-grained classification
文献类型：期刊论文
| 作者 | Lv, Le ; Zhao, Dongbin; Shao, Kun
|
| 刊名 | SOFT COMPUTING
 |
| 出版日期 | 2019-09-01 |
| 卷号 | 23期号:18页码:8711-8722 |
| 关键词 | Mid-level visual elements discovery Fine-grained classification Winner-take-all autoencoder Bipartite graph spectral partitioning |
| ISSN号 | 1432-7643 |
| DOI | 10.1007/s00500-018-3468-3 |
| 通讯作者 | Zhao, Dongbin(dongbin.zhao@ia.ac.cn) |
| 英文摘要 | In this paper, we propose a new mid-level visual elements discovery method and apply it to the fine-grained classification. We present the duality between image patches and features extracted by the convolutional winner-take-all autoencoder (CONV-WTA-AE). The sparsity constraints used by CONV-WTA-AE make a group of objects sharing the same feature components. Hence, the image patches could be clustered by their sharing feature components and the feature components can be clustered by their co-occurrence in the image patches. We propose formulating the mid-level visual elements mining as a bipartite graph partitioning problem. The spectral partitioning algorithm is employed to co-cluster image patches and feature components. The CONV-WTA-AE is an unsupervised feature learning method. Hence, it avoids using expensive annotations. Our experiments demonstrate that the spectral partitioning method is very efficient but only the confident instances in a cluster are well discriminated. The similarity metric used by this algorithm is not accurate enough. Hence, we propose training a group of linear support vector machine (SVM) to refine the clustering results. These SVMs will be trained on the initial confident instances and provide a better discriminative similarity. Then we can re-assign instances to each clusters. To avoid overfitting, this process is iterated on many data subsets. We conduct a series of experiments on the MNIST dataset to verify our algorithm. The experimental results show that our method can discover meaningful image patch clusters. In the fine-grained classification task, visual elements are input into an ensemble of convolutional neural networks. The experiments on the CompCars dataset illustrate that our method can achieve the state-of-the-art performance. |
| 资助项目 | National Natural Science Foundation of China (NSFC)[61273136] ; National Natural Science Foundation of China (NSFC)[61573353] ; National Natural Science Foundation of China (NSFC)[61533017] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000478897000031 |
| 出版者 | SPRINGER |
| 资助机构 | National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27553]  |
| 专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
| 通讯作者 | Zhao, Dongbin |
| 作者单位 | Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lv, Le,Zhao, Dongbin,Shao, Kun. Deep sparse representation-based mid-level visual elements discovery in fine-grained classification[J]. SOFT COMPUTING,2019,23(18):8711-8722. |
| APA | Lv, Le,Zhao, Dongbin,&Shao, Kun.(2019).Deep sparse representation-based mid-level visual elements discovery in fine-grained classification.SOFT COMPUTING,23(18),8711-8722. |
| MLA | Lv, Le,et al."Deep sparse representation-based mid-level visual elements discovery in fine-grained classification".SOFT COMPUTING 23.18(2019):8711-8722. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。