Loading...
Contour Detection-based Discovery of Mid-level Discriminative Patches for Scene Classification
Yang, Jinfu Zhang, Jizhao Wang, Guanghui Li, Mingai
Yang, Jinfu
Zhang, Jizhao
Wang, Guanghui
Li, Mingai
Citations
Altmetric:
Abstract
Feature extraction and representation is a key step in scene classification. In this paper, a contour detection-based mid-level features learning method is proposed for scene classification. First, a sketch tokens-based contour detection scheme is proposed to initialize seed blocks for learning mid-level patches and the patches with more contour pixels are selected as seed blocks. The procedure is demonstrated to be helpful for scene classification. Next, the seed blocks are employed to train an exemplar SVM to discover other similar occurrences and an entropy-rank criterion is utilized to mine the discriminative patches. Finally, scene categories are identified by matching the discriminative patches and testing images. Extensive experiments on the MIT Indoor-67 dataset, the 15-scene dataset and the UIUC-sports dataset show that the proposed approach yields better performance than other state-of-the-art counterparts.
Description
Date
2017-05-15
Journal Title
Journal ISSN
Volume Title
Publisher
Thieme Publishing
Research Projects
Organizational Units
Journal Issue
Keywords
Mid-level Feature, Scene Classification, Sketch Tokens, Contour Detection
Citation
Yang, J., Zhang, J., Wang, G., & Li, M. (2016). Contour Detection-Based Discovery of Mid-Level Discriminative Patches for Scene Classification. International Journal of Advanced Robotic Systems, 13(1), 30. doi:10.5772/62266