PCA plus LDA on wavelet co-occurrence histogram features: application to CBIR

TLDR: A novel wavelet based PCA-LDA approach for content Based Image Retrieval based on the co-occurrence histograms of wavelet decomposed images that exhibits superior performance in the reduced feature set.

Abstract: In this paper, we propose a novel wavelet based PCA-LDA approach for content Based Image Retrieval. The color and texture features are extracted based on the co-occurrence histograms of wavelet decomposed images. The features extracted by this method form a feature vector of high dimensionality of 1152 for the color image. A combination of Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) was applied on feature vector for dimension reduction and to enhance the class separability. By applying PCA to the feature vectors, low dimensionality feature sets were obtained and processed using LDA. The vectors obtained from the LDA are representative of each image. It is evident from the experimental results that the proposed method exhibits superior performance in the reduced feature set (i.e., retrieval efficiency 87% for proposed method, 66% for PCA and 35% for original set based on wavelet feature).