Multi-class relevance feedback for collaborative image retrieval

Abstract

In recent years, there is an emerging interest to analyse and exploit the log data recorded from different user interactions for minimising the semantic gap problem from multi-user collaborative environments. These systems are referred as "Collaborative Image Retrieval systems". In this paper, we present an approach for collaborative image retrieval using multi-class relevance feedback. The relationship between users and concepts is derived using Lin Semantic similarity measure from WordNet. Subsequently, the Particle Swarm Optimisation classifier based relevance feedback is used to retrieve similar documents. The experimental results are presented on two wellknown datasets namely Corel 700 and Flickr Image dataset. Similarly, the performance of the Particle Swarm Optimised retrieval engine is evaluated against the Genetic Algorithm optimised retrieval engine.

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

“…In recent years, there is an emerging interest to analyze and exploit the historic data from different user interactions for improving the effectiveness of retrieval results considering multi-user collaborative environments [28]. This paradigm, commonly referred to as Collaborative Image Retrieval (CIR), has attracted a lot of attention [29][30][31]. In [30], a semi-supervised distance metric learning technique integrates both log data and unlabeled data information, using a graph approach.…”

“…In [30], a semi-supervised distance metric learning technique integrates both log data and unlabeled data information, using a graph approach. An approach for collaborative image retrieval using multiclass relevance feedback and Particle Swarm Optimization classifier is proposed in [31].…”