Discriminative Soft Bag-of-Visual Phrase for Mobile Landmark Recognition

Discriminative Soft Bag-of-Visual Phrase for Mobile Landmark Recognition
Title:
Discriminative Soft Bag-of-Visual Phrase for Mobile Landmark Recognition
Other Titles:
IEEE Transactions on Multimedia
Publication Date:
01 April 2014
Citation:
Tao Chen; Kim-Hui Yap; Dajiang Zhang, "Discriminative Soft Bag-of-Visual Phrase for Mobile Landmark Recognition," Multimedia, IEEE Transactions on , vol.16, no.3, pp.612,622, April 2014 doi: 10.1109/TMM.2014.2301978 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6719514&isnumber=6766693
Abstract:
This paper proposes a new bag-of-visual phrase (BoP) approach for mobile landmark recognition based on discriminative learning of category-dependent visual phrases. Many previous landmark recognition works adopt a bag-of-words (BoW) method which ignores the co-occurrence relationship between neighboring visual words in an image. Although some works that focus on visual phrase learning have appeared, they mainly construct a generalized phrase dictionary from all categories for recognition, which lacks descriptive capability for a specific category. Another shortcoming of these works is the hard assignment of numerous feature sets to a limited number of phrases, which causes some useful feature sets to be discarded, and yields information loss. In view of this, this paper presents a discriminative soft BoP approach for mobile landmark recognition. The candidate phrases defined as adjacent pairwise codewords are first generated for each category. The important candidates are then selected through a proposed discriminative visual phrase (DVP) selection approach to form the BoP dictionary. Finally, a soft encoding method is developed to quantize each image into a BoP histogram. The context information such as location and direction captured by mobile devices is also integrated with the proposed BoP-based content analysis for landmark recognition. Experimental results on two datasets show that the proposed method is effective in mobile landmark recognition.
License type:
PublisherCopyrights
Funding Info:
Description:
ISSN:
1520-9210
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