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| Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting | |
| Wang, Jingyan（王靖琰）; Li, Yongping（李勇平）; Zhang, Ying（张莹）; Wang, Chao; Xie, Honglan; Chen, Guoling; Gao, Xin | |
| 2011 | |
| Source Publication | IEEE TRANSACTIONS ON MEDICAL IMAGING
 |
| ISSN | 0278-0062 |
| Volume | 30Issue:11Pages:1996 |
| Abstract | Bag-of-features based approaches have become prominent for image retrieval and image classification tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we first model the assignments of local descriptors as contribution functions, and then propose a novel multiple assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build contribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. We further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image retrieval tasks. The methods are tested on three well-known data sets, i.e., the ImageCLEFmed data set, the 304 CT Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term frequency weights and the term frequency-inverse document frequency weights. |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 应物所项目组 |
| WOS ID | WOS:000296455500011 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.sinap.ac.cn/handle/331007/9463 |
| Collection | 中科院上海应用物理研究所2011-2019年 |
| Recommended Citation GB/T 7714 | Wang, Jingyan,Li, Yongping,Zhang, Ying,et al. Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2011,30(11):1996. |
| APA | Wang, Jingyan.,Li, Yongping.,Zhang, Ying.,Wang, Chao.,Xie, Honglan.,...&Gao, Xin.(2011).Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting.IEEE TRANSACTIONS ON MEDICAL IMAGING,30(11),1996. |
| MLA | Wang, Jingyan,et al."Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting".IEEE TRANSACTIONS ON MEDICAL IMAGING 30.11(2011):1996. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Bag-of-Features Base（1449KB） | 开放获取 | -- | View Application Full Text | |||
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