An Intelligent mutli-object retrieval system for historical mosaics

In this work we present a Mosaics Intelligent Retrieval System (MIRS) for digital museums. The objective of this work is to attain a semantic interpretation of images of historical mosaics. We use the fuzzy logic techniques and semantic similarity measure to extract knowledge from the images for multi-object indexing. The extracted knowledge provides the users (experts and laypersons) with an intuitive way to describe and to query the images in the database. Our contribution in this paper is firstly, to define semantic fuzzy linguistic terms to encode the object position and the inter-objects spatial relationships in the mosaic image. Secondly, to present a fuzzy color quantization approach using the human perceptual HSV color space and finally, to classify semantically the mosaics images using a semantic similarity measure. The automatically extracted knowledge are collected and traduced into XML language to create mosaics metadata. This system uses a simple Graphic User Interface (GUI) in natural language and applies the classification approach both on the mosaics images database and on user queries, to limit images classes in the retrieval process. MIRS is tested on images from the exceptional Tunisian collection of complex mosaics. Experimental results are based on queries of various complexities which yielded a system’s recall and precision rates of 86.6% and 87.1%, respectively, while the classification approach gives an average success rate evaluated to 76%. Keywords—retrieval; mosaics; metadata; classification; multiobjects