Three new methods for color and texture based image matching in Content-Based Image Retrieval

Abstract

Image matching is an important and necessary process in Content-Based Image Retrieval (CBIR). We propose three new methods for image matching: the first one is based on the Local Triplet Pattern (LTP) histograms; the second one is based on the Gaussian Mixture Models (GMMs) estimated by using the Extended Mass-constraint (EMass) algorithm; and the third one is called the DCT2KL algorithm.

First, the LTP histograms are proposed to capture spatial relationships between color levels of neighboring pixels. An LTP level is extracted from each 3x3 pixel block, which is a unique number describing the color level relationship between a pixel and its neighboring pixels. Second, we consider how to represent and compare multi-dimensional color features using GMMs. GMMs are alternative methods to histograms for representing data distributions. GMMs address the high-dimensional problems from which histograms usually suffer inefficiency. In order to avoid local maxima problems in most GMM estimation algorithms, we apply the deterministic annealing method to estimate GMMs. Third, motivated by image compression algorithms, the DCT2KL method addresses the high dimensional data by using the Discrete Cosine Transform (DCT) coefficients in the YCbCr color space. The DCT coefficients are restored by partially decoding JPEG images. Assume that each DCT coefficient sequence is emitted from a memoryless source, and all these sources are independent of each other. For each target image we form a hypothesis that its DCT coefficient sequences are emitted from the same sources as the corresponding sequences in the query image. Testing these hypotheses by measuring the log-likelihoods leads to a simple yet efficient scheme that ranks each target image according to the Kullback-Leibler (KL) divergence between the empirical distribution of the DCT coefficient sequences in the query image and that in the target image.

Finally we present a scheme to combine different features and methods to boost the performance of image retrieval. Experimental results on different image data sets show that these three methods proposed above outperform the related works in literature, and the combination scheme further improves the retrieval performance.