Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6938777 | Pattern Recognition | 2018 | 12 Pages |
Abstract
A scene image is typically composed of successive background contexts and objects with regular shapes. To acquire such spatial information, we propose a new type of spatial partitioning scheme and a modified pyramid matching kernel based on spatial pyramid matching (SPM). A dense histogram of oriented gradients (HOG) is used as a low-level visual descriptor. Furthermore, inspired by the expressive coding ability of autoencoders, we also propose another approach that encodes local descriptors into mid-level features using various autoencoders. The learned mid-level features are encouraged to be sparse, robust and contractive. Then, modified spatial pyramid pooling and local normalization of the mid-level features facilitate the generation of high-level image signatures for scene classification. Comprehensive experimental results on publicly available scene datasets demonstrate the effectiveness of our methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Lin Xie, Feifei Lee, Li Liu, Zhong Yin, Yan Yan, Weidong Wang, Junjie Zhao, Qiu Chen,