Image Retrieval Using Fuzzy Color Histogram and Fuzzy String Matching: A Correlation-Based Scheme to Reduce the Semantic Gap

Abstract

The research interest in the recent years has progressed to improve the performance of image retrieval (IR) systems by reducing the semantic gap between the low-level features and the high-level concept. In this paper, we proposed an approach to combine the two modalities in IR systems, i.e., content and text, while considering the semantics between the query image and the textual query provided by the user. For content matching, color feature is extracted and is represented using fuzzy color histogram (FCH). For text matching, fuzzy string matching with edit distance is used. Furthermore, we find the correlation between the query image and the textual query provided by the user to reduce the semantic gap. Using this correlation, we combined the two modalities with late fusion approach. The proposed approach is assessed on standard annotated database. Higher values of precision and recall show better performance of the proposed approach. Moreover, the use of correlation helps in reducing the semantic gap and providing good results through better ranking of the similar images.