Rejecting mismatches of visual words by contextual descriptors
Jinliang Yao, Bing Yang, Qiuming Zhu · 2014
The Bag-of-Visual-Words model has become a popular model in image retrieval and computer vision. But when the local features of the Interest Points (IPs) are transformed into visual words in this model, the discriminative power of the local features are reduced or compromised. To address this issue, in this paper, we propose a novel contextual descriptor for local features to improve its discriminative power. The proposed contextual descriptors encode the dominant orientation and directional relationships between the reference interest point (IP) and its context. A compact Boolean array is used to represent these contextual descriptors. Our experimental results show that the proposed contextual descriptors are more robust and compact than the existing contextual descriptors, and improve the matching accuracy of visual words, thus make the Bag-of-Visual-Words model become more suitable for image retrieval and computer vision tasks.