Abstract
This paper presents a novel set of image descriptors that encodes information from color, shape, spatial and local features of an image to improve upon the popular Pyramid of Histograms of Oriented Gradients (PHOG) descriptor for object and scene image classification. In particular, a new Gabor-PHOG (GPHOG) image descriptor created by enhancing the local features of an image using multiple Gabor filters is first introduced for feature extraction. Second, a comparative assessment of the classification performance of the GPHOG descriptor is made in grayscale and six different color spaces to further propose two novel color GPHOG descriptors that perform well on different object and scene image categories. Finally, an innovative Fused Color GPHOG (FC–GPHOG) descriptor is presented by integrating the Principal Component Analysis (PCA) features of the GPHOG descriptors in the six color spaces to combine color, shape and local feature information. Feature extraction for the proposed descriptors employs PCA and Enhanced Fisher Model (EFM), and the nearest neighbor rule is used for final classification. Experimental results using the MIT Scene dataset and the Caltech 256 object categories dataset show that the proposed new FC–GPHOG descriptor achieves a classification performance better than or comparable to other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT) based Pyramid Histograms of visual Words descriptor, Color SIFT four Concentric Circles, Spatial Envelope, and Local Binary Patterns.
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References
Banerji, S., Sinha, A., Liu, C.: New image descriptors based on color, texture, shape, and wavelets for object and scene image classification. Neurocomputing 117, 173–185 (2013)
Banerji, S., Verma, A., Liu, C.: Novel color LBP descriptors for scene and image texture classification. In: 15th International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, Nevada, pp. 537–543 (2011)
Barbu, T.: Novel automatic video cut detection technique using gabor filtering. Comput. Electr. Eng. 35(5), 712–721 (2009)
Bosch, A., Zisserman, A., Munoz, X.: Scene classification via pLSA. In: The European Conference on Computer Vision, Graz, Austria, pp. 517–530 (2006)
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: The 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1–8 (2007)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: International Conference on Image and Video Retrieval, The Netherlands, Amsterdam, pp. 401–408 (2007)
Bratkova, M., Boulos, S., Shirley, P.: o RGB: a practical opponent color space for computer graphics. IEEE Comput. Graph. Appl. 29(1), 42–55 (2009)
Burghouts, G., Geusebroek, J.M.: Performance evaluation of local color invariants. Comput. Vis. Image Underst. 113(1), 48–62 (2009)
Chahooki, M.A.Z., Charkari, N.M.: Learning the shape manifold to improve object recognition. Mach. Vis. Appl. 24(1), 33–46 (2013)
Crandall, D.J., Huttenlocher, D.P.: Composite models of objects and scenes for category recognition. In: IEEE Computer Vision and, Pattern Recognition, pp. 1–8 (2007)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. The 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp. 886–893 (2005)
Daugman, J.: Complete discrete 2-d Gabor transforms by neural networks for image analysis and compression. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1169–1179 (1988)
Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)
Gonzalez, R., Woods, R.: Digital Image Processing, 3rd edn. Pearson Prentice Hall, Englewood Cliffs (2008)
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology (2007). http://authors.library.caltech.edu/7694
Hoiem, D., Efros, A.A., Hebert, M.: Putting objects in perspective. Int. J. Comput. Vis. 80(1), 3–15 (2008)
Jain, A.K., Prabhakar, S., Hong, L., Pankanti, S.: Filterbank-based fingerprint matching. IEEE Trans. Image Process. 9(5), 846–859 (2000)
Jones, J., Palmer, L.: An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J. Neurophysiol. 1233–1258 (1987)
Kong, H., Wang, L., Teoh, E.K., Li, X., Wang, J.G., Venkateswarlu, R.: Generalized 2d principal component analysis for face image representation and recognition. Neural Netw. 18(5–6), 585–594 (2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition, NY, USA, pp. 2169–2178 (2006)
Lee, H., Chung, Y., Kim, J., Park, D.: Face image retrieval using sparse representation classifier with gabor-lbp histogram. WISA, Heidelberg, pp. 273–280 (2010)
Li, L.J., Su, H., Xing, E.P., Fei-Fei, L.: Object bank: a high-level image representation for scene classification & semantic feature sparsification. In: Neural Information Processing Systems, Vancouver, Canada, pp. 1378–1386 (2010)
Liu, C.: Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 572–581 (2004)
Liu, C.: Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 725–737 (2006)
Liu, C.: Learning the uncorrelated, independent, and discriminating color spaces for face recognition. IEEE Trans. Inf. Forensics Secur. 3(2), 213–222 (2008)
Liu, C.: Extracting discriminative color features for face recognition. Pattern Recognit. Lett. 32(14), 1796–1804 (2011)
Liu, C.: Effective use of color information for large scale face verification. Neurocomputing 43–51 (2013)
Liu, C., Wechsler, H.: Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans. Image Process. 9(1), 132–137 (2000)
Liu, C., Wechsler, H.: Independent component analysis of Gabor features for face recognition. IEEE Trans. Neural Netw. 14(4), 919–928 (2003)
Liu, C., Yang, J.: ICA color space for pattern recognition. IEEE Trans. Neural Netw. 2(20), 248–257 (2009)
Lowe, D.: Object recognition from local scale-invariant features. In: The International Conference on Computer Vision, Corfu, Greece, pp. 1150–1157 (1999)
Mao, C., Gururajan, A., Sari-Sarraf, H., Hequet, E.F.: Machine vision scheme for stain-release evaluation using gabor filters with optimized coefficients. Mach. Vis. Appl. 23(2), 349–361 (2012)
Marcelja, S.: Mathematical description of the responses of simple cortical cells. J. Optic. Soc. Am. 70, 1297–1300 (1980)
Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: International Conference on Pattern Recognition, Jerusalem, Israel, pp. 582–585 (1994)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
Van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)
Shih, P., Liu, C.: Comparative assessment of content-based face image retrieval in different color spaces. Int. J. Pattern Recognit. Artif. Intell. 19(7), 1039–1048 (2005)
Shotton, J., Winn, J., Rother, C., Criminisi, A.: Textonboost: joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: European Conference on Computer Vision, pp. 1–15 (2006)
Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Ninth IEEE International Conference on Computer Vision, Nice, France, pp. 1470–1477 (2003)
Smith, A.: Color gamut transform pairs. Comput. Graph. 12(3), 12–19 (1978)
Stokman, H., Gevers, T.: Selection and fusion of color models for image feature detection. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 371–381 (2007)
Torralba, A., Murphy, K.P., Freeman, W.T., Rubin, M.A.: Context-based vision system for place and object recognition. In: The Ninth IEEE International Conference on Computer Vision, Nice, France, p. 273 (2003)
Vedaldi, A., Fulkerson, B.: Vlfeat—an open and portable library of computer vision algorithms. In: The 18th Annual ACM International Conference on Multimedia, Firenze, Italy, pp. 1469–1472 (2010)
Verma, A., Banerji, S., Liu, C.: A new color SIFT descriptor and methods for image category classification. In: International Congress on Computer Applications and Computational Science, Singapore, pp. 819–822 (2010)
Vizireanu, D., Pirnog, C., Lzrescu, V., Vizireanu, A.: The skeleton structure: an improved compression algorithm with perfect reconstruction. J. Digit. Imaging 14, 241–242 (2001)
Wang, H.: Structural two-dimensional principal component analysis for image recognition. Mach. Vis. Appl. 22(2), 433–438 (2011)
Xie, S., Shan, S., Chen, X., Chen, J.: Fusing local patterns of gabor magnitude and phase for face recognition. IEEE Trans. Image Process. 19(5), 1349–1361 (2010)
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. 73(2), 213–238 (2007)
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In: The Tenth IEEE International Conference on Computer Vision, Beijing, China, pp. 786–791 (2005)
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The authors would like to thank the associate editor and the anonymous reviewers for their critical and constructive comments, and suggestions, which helped to improve the quality of the paper.
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Sinha, A., Banerji, S. & Liu, C. New color GPHOG descriptors for object and scene image classification. Machine Vision and Applications 25, 361–375 (2014). https://doi.org/10.1007/s00138-013-0561-6
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DOI: https://doi.org/10.1007/s00138-013-0561-6