Features Conduction Neural Response and Its Application in Content-Based Image Retrieval
A novel image representation is proposed for content-based image retrieval (CBIR). The core idea of the proposed method is to do deep learning for the local features of image and to melt semantic component into the representation through a hierarchical architecture which is built to simulate human v...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2016-01-01
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| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/3908056 |
| Summary: | A novel image representation is proposed for content-based image retrieval (CBIR). The core idea of the proposed method is to do deep learning for the local features of image and to melt semantic component into the representation through a hierarchical architecture which is built to simulate human visual perception system, and then a new image descriptor of features conduction neural response (FCNR) is constructed. Compared with the classical neural response (NR), FCNR has lower computational complexity and is more suitable for CBIR tasks. The results of experiments on a commonly used image database demonstrate that, compared with those of NR related methods or some other image descriptors that were originally developed for CBIR, the proposed method has wonderful performance on retrieval efficiency and effectiveness. |
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| ISSN: | 1024-123X 1563-5147 |