cvpr cvpr2013 cvpr2013-146 cvpr2013-146-reference knowledge-graph by maker-knowledge-mining
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Author: Tim Matthews, Mark S. Nixon, Mahesan Niranjan
Abstract: We argue for the importance of explicit semantic modelling in human-centred texture analysis tasks such as retrieval, annotation, synthesis, and zero-shot learning. To this end, low-level attributes are selected and used to define a semantic space for texture. 319 texture classes varying in illumination and rotation are positioned within this semantic space using a pairwise relative comparison procedure. Low-level visual features used by existing texture descriptors are then assessed in terms of their correspondence to the semantic space. Textures with strong presence ofattributes connoting randomness and complexity are shown to be poorly modelled by existing descriptors. In a retrieval experiment semantic descriptors are shown to outperform visual descriptors. Semantic modelling of texture is thus shown to provide considerable value in both feature selection and in analysis tasks.
[1] N. Bhushan, A. R. Rao, and G. L. Lohse. The texture lexicon: Understanding the categorization ofvisual texture terms and their relationship to texture images. Cognitive Science, 21(2):219–246, 1997. 1, 2, 3, 8
[2] Y. Q. Chen, M. S. Nixon, and D. W. Thomas. Statistical geometrical features for texture classification. Pattern Recognition, 28(4):537–552, Apr. 1995. 4
[3] A. Farhadi, I. Endres, and D. Hoiem. Attribute-centric recognition for cross-category generalization. In Proc. IEEE Conf. on CVPR, pages 2352–2359, 2010. 2
[4] A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth. Describing objects by their attributes. In Proc. IEEE Conf. on CVPR, pages 1778–1785, 2009. 2
[5] J. J. Gibson. The perception of the visual world, volume xii. Houghton Mifflin, Oxford, England, 1950. 1
[6] R. Gurnsey and D. J. Fleet. Texture space. Vision Research, 41(6):745–757, Mar. 2001. 1
[7] R. Haralick. Statistical and structural approaches to texture. Proc. IEEE, 67(5):786–804, 1979. 4
[8] L. O. Harvey Jr. and M. J. Gervais. Internal representation of visual texture as the basis for the judgment of similarity. Journal ofExperimental Psychology: Human Perception and Performance, 7(4):741–753, Aug. 1981. 1, 3
[9] T. Joachims. Optimizing search engines using clickthrough data. In Proc. 8th ACM SIGKDD Int. Conf. on Knowl-
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18] edge Discovery and Data Mining, KDD ’02, pages 133–142, 2002. 4 F. A. Kingdom and D. R. Keeble. On the mechanism for scale invariance in orientation-defined textures. Vision Research, 39(8): 1477–1489, Apr. 1999. 1 A. Kovashka, D. Parikh, and K. Grauman. WhittleSearch: image search with relative attribute feedback. In Proc. IEEE Conf. on CVPR, pages 2973–2980, 2012. 3 N. Kumar, A. Berg, P. Belhumeur, and S. Nayar. Attribute and simile classifiers for face verification. In Proc. IEEE 12th Int. Conf. on Computer Vision, pages 365–372, 2009. 2 V. A. Lamme. The neurophysiology of figure-ground segregation in primary visual cortex. Journal of Neuroscience, 15(2): 1605–1615, Jan. 1995. 1 C. H. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In Proc. IEEE Conf. on CVPR, pages 951–958, June 2009. 2 K. I. Laws. Textured Image Segmentation. Ph.D. thesis, University of Southern California, 1980. 2 S.-s. Liu and M. Jernigan. Texture analysis and discrimination in additive noise. Computer Vision, Graphics, and Image Processing, 49(1):52–67, Jan. 1990. 4 B. Manjunath and W. Ma. Texture features for browsing and retrieval of image data. IEEE Trans. on Pattern Analysis and Machine Intelligence, 18(8):837 –842, Aug. 1996. 4 T. Ojala, T. Maenpaa, M. Pietikainen, J. Viertola, J. Kyllonen, and S. Huovinen. Outex - new framework for empirical evaluation of texture analysis algorithms. In Proc. 16th Int. Conf. on Pattern Recognition, volume 1, pages 701–706, 2002. 2
[19] T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(7):971–987, 2002. 4
[20] A. Oliva, A. B. Torralba, A. Gurin-Dugu, and J. Hrault. Global semantic classification of scenes using power spectrum templates. In Proc. Int. Conf. on Challenge of Image Retrieval, Electronic Workshops in Computing series, 1999. 2
[21] D. Parikh and K. Grauman. Relative attributes. In IEEE Int. Conf. on Computer Vision, pages 503–5 10, 2011. 1, 3, 4
[22] C. J. Price and G. W. Humphreys. The effects of surface detail on object categorization and naming. The Quarterly Journal of Experimental Psychology, 41(4):797–828, 1989. 1
[23] A. Rao and G. Lohse. Towards a texture naming system: Identifying relevant dimensions of texture. In Proc. IEEE Conf. on Visualization, pages 220–227, 1993. 1, 3
[24] D. A. Reid and M. Nixon. Using comparative human descriptions for soft biometrics. In Proc. Int. Joint Conference on Biometrics, pages 1–6, Oct. 2011. 2, 3
[25] B. E. Rogowitz, T. Frese, J. Smith, C. A. Bouman, and E. Kalin. Perceptual image similarity experiments. In Proc. SPIE Conf. on Human Vision and Electronic Imaging, pages 576–590, 1998. 2
[26] S. Samangooei, B. Guo, and M. Nixon. The use of semantic human description as a soft biometric. In Proc. 2nd IEEE Int. Conf. on Biometrics: Theory, Applications and Systems, pages 1–7, Oct. 2008. 1, 2
[27] N. Serrano, A. E. Savakis, and J. Luo. Improved scene classification using efficient low-level features and semantic cues. Pattern Recognition, 37(9): 1773–1784, Sept. 2004. 1
[28] H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Trans. on Systems, Man and Cybernetics, 8(6):460–473, June 1978. 2, 3
[29] J. Vogel and B. Schiele. Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision, 72(2): 133–157, Apr. 2007. 1
[30] J. Zhang and T. Tan. Briefreview ofinvariant texture analysis methods. Pattern Recognition, 35(3):735–747, Mar. 2002. 1 11111222225555535533