ImageHunter: a novel tool for Relevance Feedback in Content Based Image Retrieval

TitleImageHunter: a novel tool for Relevance Feedback in Content Based Image Retrieval
Publication TypeConference Paper
Year of Publication2011
AuthorsTronci, R, Murgia, G, Pili, M, Piras, L, Giacinto, G
Conference Name5th International Workshop on New Challenges in Distributed Information Filtering and Retrieval (DART)
Date Published17/09/2011
Conference LocationPalermo, Italy

Nowadays, a very large number of digital image archives is easily produced thanks to the wide diffusion of personal digital cameras and mobile devices with embedded cameras. Thus, each personal computer, personal storage unit, as well as photo-sharing and social-network web sites, are rapidly becoming the repository for thousands, or even billions of images (i.e., more than 100 million photos are uploaded every day on the social site Facebook). As a consequence, there is an increasing need for tools enabling the semantic search, classification, and retrieval of images. The use of meta-data associated to images solves the problems only partially, as the process of assigning reliable meta data to images is not trivial, is slow, and closely related to whom performed the task. One solution for effective image search and retrieval is to combine content-based analysis with feedbacks from the users. In this paper we present Image Hunter, a tool that implements a Content Based Image Retrieval (CBIR) engine with a Relevance Feedback mechanism. Thanks to a user friendly interface the tool is especially suited to unskilled users. In addition, the modular structure permits the use of the same core both in web-based and stand alone applications.

Citation Key 1198
13 - DART-CHAPTER 2011.pdf1.31 MB