Image Retrieval: History, Current Approaches, and Promising Framework

Abstract Today, by dominant use of the world computer networks, the volume of image database is increased and retrieving the required image similar with the image is a serious need. Here having a dynamic and flexible framework can help considerably in the design of an image retrieval system with high accuracy. In this study, by the investigation and analysis of three systems of current famous systems of retrieving and emphasis on weaknesses and strengths of the systems, presented a general framework for image retrieval systems. The important issue is that an ideal image retrieval system should be able to automatically extract semantic content and make the images indexing

An efficiency comparison of two content-based image retrieval systems, GIFT and PicSOM

Abstract. Content-based image retrieval (CBIR) addresses the problem of assisting a user to retrieve images from unannotated databases, based on features that can be automatically derived from the images. Today, there exists several CBIR systems based on different methods. Only few attemps to benchmark these have been made, although the usefulness of benchmarking is undeniable in the development of different algorithms. In this paper we publish our benchmarking results of two CBIR systems with different implementation methods. The CBIR systems in question are GIFT (University of Geneva) and PicSOM (Helsinki University of Technology). The results clearly show that our PicSOM system, which we earlier have not been able to benchmark against other CBIR systems, comes off well in the comparison. Also, the results indicate that tests based on a single ground truth class are not enough for fair system comparisons.