OPTIMIZATION OF MINING HISTOGRAMIC SIGNS DETECTION AND RECOGNITION SYSTEM

Content-based image retrieval (CBIR) systems are the most widely used feature of an image colouring. Basically, these systems try to retrieve images similar to a user-defined specification or pattern (e.g., shape sketch, image example). Their goal is to support image retrieval based on content properties (e.g., shape, color, texture), usually encoded into feature vectors. One of the main advantages of the CBIR approach is the possibility of an automatic retrieval process, instead of the traditional keyword-bas ed approach, which usually requires very laborious and time-consuming previous annotation of database images. This paper proposes a Internet image search approach in which search algorithm makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user’s intention effectively. This paper aims to introduce the problems and challenges concerned with the creation of CBIR systems, to describe the existing solutions and applications, and to present the state of the art of the existing research area