Textural Features and Relevance Feedback for Image Retrieval

This paper focuses on the retrieval of complex images based on their textural content. We use GMRF for texture discrimination and a region-growing algorithm for texture segmentation. Relevance feedback is introduced to improve retrieval accuracy. Keywords Textures, relevance feedback, image features, Gaussian Markovian Random Fields. 1 INTRODUCTION Content-based image classification, indexing and retrieval requires semantic interpretation and cannot be afforded with current technology. A surrogate of semantic interpretation is the computation of visual features that can be used as quantitative parameters for the identification of similar images. Thus, the problem of retrieving images with some content is substituted with the problem of retrieving images visually close to a target one. Image features like colors, contours, textures, have been used in various systems. Related work includes, but it is not limited to Flickner (1995), Bach (1996), Ma (1997), Popat (1997), Rui (1997), Cel..