Extracting discriminative features for CBIR
Extracting discriminative features for CBIR
复制标题
提取 CBIR 的判别特征
DOI：
10.1007/s11042-011-0836-8
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发表时间：
2012-11
影响因子：
3.6
通讯作者：
Zhongzhi Shi
中科院分区：
文献类型：
--
作者：
Zhiping Shi;Xi Liu;Qingyong Li;Qing He;Zhongzhi Shi
Developing low-dimensional discriminative features is crucial for content-based image retrieval (CBIR). In this paper, we present a square symmetrical local binary pattern (SSLBP) texture descriptor, which is a compact symmetrical-invariant variation of local binary pattern (LBP), then we propose a merging 2-class linear discriminant analysis (M2CLDA) method to capture low-dimensional optimal discriminative features in the projection space. M2CLDA calculates discriminant vectors with respect to each class in the one-vs.-all classification scenario and then merges all the discriminant vectors to form a projection matrix. The dimensionality of the M2CLDA space fits in with the number of classes involved. Our experiments show that the SSLBP feature is an effective variation of LBP, and the M2CLDA approach improves the performance of image retrieval and image classification observably as compared with the existed LDA approaches and takes less computation complexity than the kernel discriminant analysis (KDA) methods.
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