Learning the Optimal Transformation of Salient Features for Image Classification
In this paper, we address the problem of recovering an optimal salient image descriptor transformation for image classification. Our method involves two steps. Firstly, a binary salient map is generated to specify the regions of interest for subsequent image feature extraction. To this end, an optimal cut-off value is recovered by maximising Fisher's linear discriminant separability measure so as to separate the salient regions from the background of the scene. Next, image descriptors are...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the Digital Image Computing: Techniques and Applications (DICTA 2009)|
|01_Zhou_Learning_the_Optimal_2009.pdf||263.15 kB||Adobe PDF||Request a copy|
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