Understanding User’s Intention in Semantic Based Image Retrieval: Combining Positive and Negative Examples

Abstract

Understanding user’s intention is at the core of an effective images retrieval systems. It still a significant challenge for current systems, especially in situations where user’s needs are ambiguous. It is in this perspective that fits our study. In this paper, we address the challenge of grasping user’s intention in semantic based images retrieval. We propose an algorithm that performs a thorough analysis of the semantic concepts presented in user’s query. The proposed algorithm is based on an ontology and takes into account the combination of positive and negative examples. The positive examples are used to perform generalization and the negative examples are used to perform specialization which considerably decrease the two famous problems of image retrieval: noise and miss. Our algorithm processed in two steps: in the first step, we deal only with the positive examples where we will generalize the query from the explicit concepts to infer the others hidden concepts desired by the user. whereas the second step deal with the negative examples to refine results obtained in the first step. We created an image retrieval system based on the proposed algorithm. Experimental results show that our algorithm could well capture user’s intention and improve significantly precision and recall.