Abstract
For metrological purposes, distance between texture images is crucial. This work study as a pair the couple texture feature/similarity measure. Starting from the Colour Contrast Occurrence Matrix (\(C_{2}O\)) definition, we propose an adapted similarity measure improving texture retrieval. In a second step, we propose a modified version of the \(C_2O\) definition including the texture’s colour average inside a modified similarity measure. Performance in texture retrieval is assessed for four challenging datasets: Vistex, Stex, Outex-TC13 and KTH-TIPS2b databases facing to the recent results from the state-of-the-art. Results show the high efficiency of the proposed approach based on a simple pair feature/similarity measure facing to more complex approaches including Convolutional Neural Networks.
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Notes
- 1.
The first Julesz conjecture states that preattentive discrimination of textures is possible only for textures that differ on second-order correlation statistics.
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Jebali, H., Richard, N., Chatoux, H., Naouai, M. (2018). Relocated Colour Contrast Occurrence Matrix and Adapted Similarity Measure for Colour Texture Retrieval. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_51
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