期刊
INTERNATIONAL JOURNAL OF COMPUTER VISION
卷 120, 期 1, 页码 1-13出版社
SPRINGER
DOI: 10.1007/s11263-016-0889-2
关键词
Image search; BoW model; Convolutional neural network; Contextual evidences
资金
- National High Technology Research and Development Program of China (863 program) [2012AA011004]
- National Science and Technology Support Program [2013BAK02B04]
- ARO [W911NF-15-1-0290]
- NEC Laboratories of America and Blippar
- National Science Foundation of China (NSFC) [61429201]
This paper considers the task of image search using the Bag-of-Words (BoW) model. In this model, the precision of visual matching plays a critical role. Conventionally, local cues of a keypoint, e.g., SIFT, are employed. However, such strategy does not consider the contextual evidences of a keypoint, a problem which would lead to the prevalence of false matches. To address this problem and enable accurate visual matching, this paper proposes to integrate discriminative cues frommultiple contextual levels, i.e., local, regional, and global, via probabilistic analysis. True match is defined as a pair of keypoints corresponding to the same scene location on all three levels (Fig. 1). Specifically, the Convolutional Neural Network (CNN) is employed to extract features from regional and global patches. We show that CNN feature is complementary to SIFT due to its semantic awareness and compares favorably to several other descriptors such as GIST, HSV, etc. To reduce memory usage, we propose to index CNN features outside the inverted file, communicated by memory-efficient pointers. Experiments on three benchmark datasets demonstrate that our method greatly promotes the search accuracy when CNN feature is integrated. We show that our method is efficient in terms of time cost compared with the BoW baseline, and yields competitive accuracy with the state-of-the-arts.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据