Feature extraction and relevance evaluation for heterogeneous image database recognition

Rostom Kachouri (1) , Khalifa Djemal (1) , Hichem Maaref (1) , Dorra Masmoudi (2) , Nabil Derbel (2)

1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
2 CIELS - Research unit on Computers, Imaging, Electronics and Systems

Content-based image retrieval (CBIR) techniques are becoming increasingly important in various fields. One of the most important steps in CBIR systems is feature extraction. However, using not appropriate features in heterogeneous image database during retrieval process does not provide a complete description of an image. Indeed, each feature is able to describe some characteristics related to the shape, the color or the texture of the objects in image, but it can not cover the entire visual characteristics of the image. Therefore, many researchers have explored the use of multiple features to describe an image. In this paper, we propose the extraction and the relevance evaluation of several features for an heterogeneous image database classification and recognition, then we study the image retrieval system effectiveness with a new hierarchical feature model. The obtained results prove that using the new hierarchical feature model is more efficient than the use of the classical aggregated features in an image retrieval system.

Mots clés: CBIR; Feature extraction; Feature relevance evaluation; Hierarchical feature model; Heterogeneous image database; SV-multticlass

Rostom Kachouri, Khalifa Djemal, Hichem Maaref, Dorra Masmoudi, Nabil Derbel. Feature extraction and relevance evaluation for heterogeneous image database recognition. First Workshops on Image Processing Theory, Tools and Applications (IPTA 2008), Nov 2008, Sousse, Tunisia. (elec. proc), ⟨10.1109/IPTA.2008.4743738⟩. ⟨hal-00654739⟩