Gabor Wavelets and GVF Functions for Feature Extraction in Efficient Content Based Colour and Texture Images Retrieval

Texture and shape information have been the primitive image features in content based image retrieval systems. This paper presents a novel framework for combining the texture and shape information, and achieves higher retrieval efficiency. The image is partitioned into non-overlapping tiles of different size. The features drawn from transferred image with Gabor wavelets using first and second moments between the image tiles, serve as local descriptors of texture. This local information is captured for two resolutions and two grid layouts that provide different details of the same image. Shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the texture features between image and conjunction with the shape features provide a robust feature set for image retrieval. The experimental results show the efficacy of the method. For matching the images an integrated matching scheme, based on most similar highest priority (MSHP) principle is provided. The experimental results are compared with previous works and are found to be encouraging.