Class-specific image representation for image classification using multiple scale-invariant region detectors
SCIE
SCOPUS
- Title
- Class-specific image representation for image classification using multiple scale-invariant region detectors
- Authors
- Lee, Hui-Jin; Hong, Ki-Sang
- Date Issued
- 2017-08
- Publisher
- SPRINGER
- Abstract
- We propose a new class-specific image representation for image classification using multiple region detectors. The new representation is designed to solve the problem of increasing variation in object location and size within images of a class, for which traditional spatial pyramid matching shows limited classification accuracy. We propose a new region-division method that divides the image region into two class-specific regions, called class-specific region-of-interest (C-ROI) and focal region (FR). Using multiple region detectors and appropriate mixing of their responses avoids the problem of selecting a region detector that gives the best classification accuracy for a given image class, and thereby yields better results than using only one region detector. Several scale-invariant region detectors are used to obtain C-ROI and FR by considering their importance over a given image class. In experiments using several well-known datasets, the proposed method improved the accuracy and achieved results that were better than or comparable to those achieved by the related methods.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/92060
- DOI
- 10.1007/s10044-016-0529-z
- ISSN
- 1433-7541
- Article Type
- Article
- Citation
- PATTERN ANALYSIS AND APPLICATIONS, vol. 20, no. 3, page. 717 - 732, 2017-08
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