Personalized Open-Vocabulary Image Retrieval via Semantic and SRSiM-Based Social Features

The large scale of image databases opens up new challenges for storing, organizing, and searching for data. The common challenge of image retrieval systems is to emphasize object detection and recognition from images to bridge the semantic gap between the Open-Vocabulary and Visual Content. This paper proposes a framework for a personalized image retrieval system which selects the most relevant images according to the specified query, the user interest, and the semantic interpretation. Our framework is composed of three major modules: (1) Users processing using Movielens as a social database. (2) Query processing and personalization. (3) Images processing by semantic annotation and clustering based on a new semantic similarity measure named SRSiM (Statistical Retina Similarity Measure) using statistical and knowledge-based approaches. The experimental study is performed on the NUS-WIDE dataset, a list of queries covering several social subjects and various user profiles extracted from the MovieLens database. The evaluation of the overall system shows promising results going up to 0.675 in terms of MAP (Mean Average Precision) measurement, which overcomes the score of other works established in the same conditions and using the same databases. The SRSiM measurement also reaches an accuracy of 77.77\% compared to Wordnet, Wikipedia, and Retina-based measurements