Abstract
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, personal computers, personal storage units, as well as photo-sharing and social-network websites, 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 chapter 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.
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Tronci, R., Murgia, G., Pili, M., Piras, L., Giacinto, G. (2013). ImageHunter: A Novel Tool for Relevance Feedback in Content Based Image Retrieval. In: Lai, C., Semeraro, G., Vargiu, E. (eds) New Challenges in Distributed Information Filtering and Retrieval. Studies in Computational Intelligence, vol 439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31546-6_4
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