Abstract
A method of attributing an image to a particular category from a large collection of images is stated as Image Classification. In this paper, we propose diverse subspace techniques, which concentrate on consistency and orientations of an image, extracting color, shape, and texture data with orthogonal transformation into uncorrelatedĀ space. Initially, preprocessing is done by transforming image to HSV color space as it is similar to human color perception property. Later, most informative score features are obtained using PCA, MPCA, KPCA, and GPCA with linear and nonlinear projection onto lower dimensional space which are further classified using diverse similarity measures and neural networks. The performance analysis is carried out on large multi-class datasets such as Corel-1K, Caltech-101, and Caltech-256 and the improvised correctness rate is witnessed in comparison with several benchmarking methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Swain, M.J., Ballard, D.H.: Indexing via color histograms. In: Proceedings of 3rd International Conference Computer Vision, pp. 11ā32 (1991)
Stricker, M., Orengo, M.: Similarity of color images. Storage retrieval image video database. In: Proceedings SPIE, pp. 381ā92 (1995)
Holub, A., Welling, M., Perona, P.: Exploiting unlabelled data for hybrid object classification. In: National Information Processing System Workshop on Inter-Class Transfer (2005)
Wang, X., Wang, Z.: A novel method for image retrieval based on structure elementās descriptor. J. Vis. Commun. Image Represent. 24, 63ā74 (2013)
Wang, X., Chen, Z., Yun, J.: An effective method for color image retrieval based on texture. Comput. Stan. Interf. 34, 31ā35 (2012)
Jagpal, S., Jashanbir, S.K., Reecha, S.: Different approaches of CBIR techniques. Int. J. Comput. Distrib. Syst. 1(2), 76ā78 (2012)
Mutch, J., Lowe, D.G.: Multiclass Object Recognition with Sparse, Localized Features, vol. 1, pp. 11ā18. IEEEāCVPR (2006)
Fei-Fei, L., Fergus, R., Perona, P.: An incremental bayesian approach testing on 101 objects categories. In: Workshop on Generative-Model Based Vision. CVPR (2004)
Wang, J., Yang, J., Huang, T., Gong, Y.: Locality Constraint Linear Coding for Image Classification, pp. 1063ā6919. CVPR (2010)
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, IEEE, pp. 1794ā1801 (2009)
Liu, B.-D., Wang, Y.-X., Zhang, Y.-J., Shen, B.: Learning dictionary on manifolds for image classification. Pattern Recogn. 46, 1879ā1890 (2013). (Elsevier)
Mahantesh, K., Manjunath Aradhya, V.N., Niranjan, S.K.: Coslets: A Novel Approach to Explore Object Taxonomy in Compressed DCT Domain for Large Image Datasets, vol. 320, pp. 39ā48. Springer (2015)
Anusha, T.R., Hemavathi, N., Mahantesh, K., Chetana, R.: An investigation of combining gradient descriptor and diverse classifiers to improve object taxonomy in very large image dataset. In: International Conference on Contemporary Computing and Informatics, IEEE, pp. 581ā585 (2014)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71ā86 (1991)
Pankaj, D.S., Wilscy, M.: Comparison of PCA, LDA & Gabor Features for Face Recognition Using Neural Networks, vol. 177, pp. 413ā422 (2013)
Preeti Rai, Prithee Khanna.:Ā An Illumination, Expression & Noise Invariant Gender Classifier Using 2D-PCA on Real Gabor Space, pp. 15ā28. Elseiver (2015)
Shlens, J.: A Tutorial on PCA Derivation, Discussion and SVD (2003)
Gottumukkal, R., Asari, V.K.: An Improved Face Recognition Technique Based on Modular PCA Approach, pp. 429ā436 (2004)
Wang, Q.: Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models. CVPR (2012)
Wasserman, P.D.: Adv. Methods Neural Comput. 155ā61 (1993)
Thanwari, P.B., Janwe, N.J.: CBIR based on color and texture. Int. J. Inf. Technol. Knowl. Manage. 4, 99ā132 (2011)
Griffin, G., Holub, A., Perona, P.: Caltech-256 Object Category Dataset (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2016 Springer India
About this paper
Cite this paper
Hemavathi, N., Anusha, T.R., Mahantesh, K., Manjunath Aradhya, V.N. (2016). An Investigation of Gabor PCA and Different Similarity Measure Techniques for Image Classification. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 381. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2526-3_3
Download citation
DOI: https://doi.org/10.1007/978-81-322-2526-3_3
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2525-6
Online ISBN: 978-81-322-2526-3
eBook Packages: EngineeringEngineering (R0)