Performances of Mobile-Agents for Interactive Image Retrieval

In this paper, we present a system for image retrieval over a network of computer based on "ant-like" mobile-agents. Image databases are hosted on the network, and the user wants to find all the images matching a specific concept (cars, flower, Italy, etc...). Usually, content based image retrieval systems (CBIR) do not consider the dispertion of the data among the network. We train a SVM classifier with examples annotated by the user and then launch mobile agents which explore the network in order to retrieve the most relevant images. Several interactive session (launching of agents then annotation of the results) are made to improve the classifier. Experiments are made both to see the influence of localization of the search concept on the quality of the learning, and to focus on the quality of the agent based solution compared to a centralizing system within a fixed amount of time for the interaction