Image Retrieval Measures Based on Illumination Invariant Textural MRF Features

Content-based image retrieval (CBIR) systems, target database images using feature similarities with respect to the query. We introduce fast and robust image retrieval measures that utilise novel illumination invariant features extracted from three different Markov random field (MRF) based texture representations. These measures allow retrieving images with similar scenes comprising colour-textured objects viewed with different illumination brightness or spectrum. The proposed illumination insensitive measures are compared favourably with the most frequently used features like the Local Binary Patterns, steerable pyramid and Gabor textural features, respectively. The superiority of these new illumination invariant measures and their robustness to added noise are empirically demonstrated in the illumination invariant recognition of textures from the Outex database.


Original textures were acquired from Outex database. Resolution 100dpi, angle 00, all illuminations.

Video of Texture retrieval (65MB, 4:18, XDIV AVI):   part 1 (noise),   part 2,   part 3,   part 4  


Vácha, P., and M. Haindl, "Image retrieval measures based on illumination invariant textural MRF features", CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval, New York, NY, USA, ACM Press, pp. 448–454, 2007.