Graph-Based Image Retrieval: State of the Art

Abstract

The paper deals with the problem of semantic Image Retrieval. Indeed, the image has recently gained popularity in several domains such as medical domain, marketing, etc. Image plays a very vital role in documentation. However, finding visual and relevant information in an image is a huge task for Image Retrieval community and a very discussed issue in digital image processing. In fact, image can be extracted from a big collection of images, in the purpose of responding to user's need. Image Retrieval processes based on classical techniques may not be sufficient to user. For several years, great efforts have been devoted to integrate semantic aspect, in order to enhance relevance of the result and ensure high-level content consideration in image. This paper presents a state of the art of Image Retrieval approaches using graph theory due to the growing interest given to graphs in terms of performance, representation and its ability to ingrate semantic aspect. We review a number of recently available graph-based approaches in Image Retrieval aiming to determine factors adding semantic aspect in Image Retrieval system.