Adaptive relevant feature selection and hierarchical image classification in heterogeneous databases
Abstract
In heterogeneous databases, images often provided from different sources and belong to different topics, hence there is a need for a large description to ensure efficient representation of their content. However, extracted features are not always adapted to the considered image database. In this paper we propose a new image recognition approach based on two innovations, namely adaptive feature selection and Multi-Model Classification Method (MC-MM). The adaptive selection considers only the most adapted features with the used image database content. The MC-MM method ensures image recognition using hierarchically selected features. Experimental results confirm the effectiveness and the robustness of our proposed approach.