Textile Image Retrieval Using Composite Feature Vectors of Color and Wavelet Transformed Textural Property
Jun Chul Chun, Wong Gi Kim · Applied Mechanics and Materials · 2013
It is known that wavelet transform provides very useful feature values in analyzing various types of images. This paper presents a novel approach for content-based textile image retrieval which uses composite feature vectors of low-level color feature from spatial domain and second-order statistic features from wavelet-transformed sub-band coefficients. Even though color histogram itself is efficient and most used signature for CBIR, it is unable to carry local spatial information of pixel and generate inaccurate retrieval results especially in large image data set. In this paper, we extract texture features such as contrast, homogeneity, ASM(angular-second momentum) and entropy from decomposed sub-band images by wavelet transform and utilize these multiple feature vector to retrieve textile images combining with color histogram. From the experimental results it is proven that the proposed approach is efficiently retrieve the desired images from a large set of textile image database.