Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4969150 | Information Fusion | 2017 | 9 Pages |
Abstract
The relationship between visual words and local feature (words structure) or the distribution among images (images structure) is important in feature encoding to approximate the intrinsically discriminative structure of images in the Bag-of-Words (BoW) model. However, in recently most methods, the intrinsic invariance in intra-class images is difficultly captured using words structure or images structure for large variability image classification. To overcome this limitation, we propose a local visual feature coding based on heterogeneous structure fusion (LVFC-HSF) that explores the nonlinear relationship between words structure and images structure in feature space, as follows. First, we utilize high-order topology to describe the dependence of the visual words, and use the distance measurement based on the local feature to represent the distribution of images. Then, we construct the unitedly optimal framework according to the relevance between words structure and images structure to solve the projection matrix of local feature and the weight coefficient, which can exploit the nonlinear relationship of heterogeneous structure to balance their interaction. Finally, we adopt the improving fisher kernel(IFK) to fit the distribution of the projected features for obtaining the image feature. The experimental results on ORL, 15 Scenes, Caltech 101 and Caltech 256 demonstrate that heterogeneous structure fusion significantly enhances the intrinsic structure construction, and consequently improves the classification performance in these data sets.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Guangfeng Lin, Caixia Fan, Hong Zhu, Yalin Miu, Xiaobing Kang,