Abstract
State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task.
In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets.
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References
Khan, F.S., van de Weijer, J., Vanrell, M.: Modulating shape features by color attention for object recognition. IJCV 98(1), 49–64 (2012)
Khan, F.S., Anwer, R.M., van de Weijer, J., Bagdanov, A.D., Vanrell, M., Lopez, A.M.: Color attributes for object detection. In: CVPR (2012)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. PAMI 32(9), 1582–1596 (2010)
Treisman, A., Gelade, G.: A feature integration theory of attention. Cogn. Psych. 12, 97–136 (1980)
Wolfe, J.M.: Watching single cells pay attention. Science 308, 503–504 (2005)
Khan, F.S., van de Weijer, J., Bagdanov, A.D., Vanrell, M.: Portmanteau vocabularies for multi-cue image representations. In: NIPS (2011)
Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. PAMI 27(8), 1265–1278 (2005)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI 24(7), 971–987 (2002)
Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. IJCV 62(2), 61–81 (2005)
Lee, S.H., Choi, J.Y., Ro, Y.M., Plataniotis, K.: Local color vector binary patterns from multichannel face images for face recognition. TIP 21(4), 2347–2353 (2012)
Topi Maenpaa, M.P.: Classification with color and texture: jointly or separately? PR 37(8), 1629–1640 (2004)
Susana Alvarez, M.V.: Texton theory revisited: A bag-of-words approach to combine textons. PR 45(12), 4312–4325 (2012)
van de Weijer, J., Schmid, C.: Coloring local feature extraction. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 334–348. Springer, Heidelberg (2006)
van de Weijer, J., Schmid, C., Verbeek, J.J., Larlus, D.: Learning color names for real-world applications. TIP 18(7), 1512–1524 (2009)
Dhillon, I., Mallela, S., Kumar, R.: A divisive information-theoretic feature clustering algorithm for text classification. JMLR 3, 1265–1287 (2003)
Elfiky, N., Khan, F.S., van de Weijer, J., Gonzalez, J.: Discriminative compact pyramids for object and scene recognition. PR 45(4), 1627–1636 (2012)
Oliva, A., Torralba, A.B.: Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV 42(3), 145–175 (2001)
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Khan, F.S., van de Weijer, J., Ali, S., Felsberg, M. (2013). Evaluating the Impact of Color on Texture Recognition. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_18
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DOI: https://doi.org/10.1007/978-3-642-40261-6_18
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