Multivariate texture retrieval using the SIRV representation and the geodesic distance

Lionel Bombrun (1) , Nour-Eddine Lasmar (1) , Yannick Berthoumieu (1) , Geert Verdoolaege (2)

1 IMS - Laboratoire de l'intégration, du matériau au système
2 Department of Applied physics [Ghent]

This paper presents a new wavelet based retrieval approach based on Spherically Invariant Random Vector (SIRV) modeling of wavelet subbands. Under this multivariate model, wavelet coefficients are considered as a realization of a random vector which is a product of the square root of a scalar random variable (called multiplier) with an independent Gaussian vector. We propose to work on the joint distribution of the scalar multiplier and the multivariate Gaussian process. For measuring similarity between two texture images, the geodesic distance is provided for various multiplier priors. A comparative study between the proposed method and conventional models on the VisTex image database is conducted and indicates that SIRV modeling combined with geodesic distance achieves higher recognition rates than classical approaches.

Mots clés: Texture; Multiscale analysis; Kullback-Leibler divergence; Geodesic distance; Spherically Invariant Random Vector

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Lionel Bombrun, Nour-Eddine Lasmar, Yannick Berthoumieu, Geert Verdoolaege. Multivariate texture retrieval using the SIRV representation and the geodesic distance. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, Prague, Czech Republic. pp.865 - 868, ⟨10.1109/ICASSP.2011.5946541⟩. ⟨hal-00661682⟩