＜正＞This paper proposes an adaptive image retrieval method via spatial-frequency mixed features（SFMF）. The SFMF...
Xiaohui Yang,Xueyan Yao,Dengfeng Li,Lijun Cai Institute of Applied Mathematics,School of Mathematics and Information Sciences,Henan University,475004 Kaifeng
＜正＞This paper presents a method for extracting texture and color hybrid features and constructing an adaptive ...
Xiaohui Yang,Zhongye Wang,Dengfeng Li,Jing Zhang Institute of Applied Mathematics,School of Mathematics and Information Sciences,Henan University,475004 Kaifeng
Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images classification system, is still low. In this paper, we propose a local texture descriptor termed fan refined local binary pattern, which captures more detailed information by integrating the spatial distribution into the local binary pattern feature. We compare our approach with different methods on a subset of product images on Amazon/e Bay and parts of PI100 and experimental results have demonstrated that our proposed approach is superior to the current existing methods. The highest classification precision is increased by 21% and the average classification time is reduced by 2/3.
A directional filter algorithm for intensity synthetic aperture radar (SAR) image based on nonsubsampled contourlet transform (NSCT) and immune clonal selection (ICS) is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.
