Instance-Based Relevance Feedback for Image Retrieval

High retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. These mechanisms require that the user judges the quality of the results of the query by marking all the retrieved images as being either relevant or not. Then, the search engine exploits this information to adapt the search to better meet user's needs. At present, the vast majority of proposed relevance feedback mechanisms are formulated in terms of search model that has to be optimized. Such an optimization involves the modification of some search parameters so that the nearest neighbor of the query vector contains the largest number of relevant images. In this paper, a different approach to relevance feedback is proposed. After the user provides the first feedback, following retrievals are not based on knn search, but on the computation of a relevance score for each image of the database. This score is computed as a function of two distances, namely the distance...

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Giorgio Giacinto, Fabio Roli
• Keywords: Content-based Image Retrieval, Image, NIPS 2004, NIPS 2007, Relevance Feedback Mechanisms