International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 8, No. 8, 8 Aug. 2016
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Image Classification Using Fusion of Holistic Visual Descriptions
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Author(s)
Index Terms
Scene Classification, Feature Fusion, Image Mining, Low-level features, Kappa Statistic
Abstract
An efficient approach for scene classification is necessary for automatically labeling an image as well as for retrieval of desired images from large scale repositories. In this paper machine learning and computer vision techniques have been applied for scene classification. The system is based on feature fusion method with holistic visual color, texture and edge descriptors. Color moments, Color Coherence Vector, Color Auto Correlogram, GLCM, Daubechies Wavelets, Gabor filters and MPEG-7 Edge Direction Histogram have been used in the proposed system to find the best combination of features for this problem. Two state-of-the-art soft computing machine learning techniques: Support vector machine (SVM) and Artificial Neural Networks have been used to classify scene images into meaningful categories. The benchmarked Oliva-Torralba dataset has been used in this research. We report satisfactory categorization performances on a large data set of eight categories of 2688 complex, natural and urban scenes. Using a set of exhaustive experiments our proposed system has achieved classification accuracy as high as 92.5% for natural scenes (OT4) and as high as 86.4% for mixed scene categories (OT8). We also evaluate the system performance by predictive accuracy measures namely sensitivity, specificity, F-score and kappa statistic.
Cite This Paper
Padmavati Shrivastava, K. K. Bhoyar, A.S. Zadgaonkar,"Image Classification Using Fusion of Holistic Visual Descriptions", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.8, pp.47-57, 2016. DOI: 10.5815/ijigsp.2016.08.07
Reference
[1]Ji Zhang, Wynne Hsu, Mong Li Lee, "Image Mining: Issues, Frameworks and Techniques", Proceedings of the Second International Workshop on Multimedia Data Mining (MDM/KDD'2001), in conjunction with ACM SIGKDD conference, San Francisco, USA, 26th August,2001.
[2]M. M. Gorkani and R. W. Picard, "Texture orientation for sorting photos at a glance", in Int. Conf. Pattern Recognition, Vol. 1, Oct. 1994, pp. 459–464.
[3]Lalit Gupta, Vinod Pathangay, Arpita Patra, A. Dyana and Sukhendu Das, "Indoor vs. Outdoor Scene Classification using Probabilistic Neural Network", EURASIP Journal on Advances in Signal Processing, Special Issue on Image Perception, Volume 2007, Issue 1, pp. 1-10.
[4]Amitabh Wahi, Sundaramurthy S., "Wavelet - Based Classification of Outdoor Natural Scenes by Resilient Neural Network", World Academy of Science, Engineering and Technology, International Journal of Computer, Control, Quantum and Information Engineering, Vol 8, No: 9, 2014.
[5]Li Tao, Yeong – Hwa Kim, Yeong – Taeg Kim, "An efficient neural network based indoor-outdoor scene classification algorithm", International Conference on Consumer Electronics (ICCE), Digest of Technical Papers(2010)., pp 317-318.
[6]J. Vogel and B. Schiele, "A semantic typicality measure for natural scene categorization", In DAGM'04 Annual Pattern Recognition Symposium, Tuebingen, Germany, 2004.
[7]Han, Y., Liu, G., "A Hierarchical GIST model embedding multiple biological feasibilities for scene classification", In: Proc IAPR, Int. Conf. Pattern Recognition, Istanbul, (2012), pp. 3109–3112.
[8]A. Vailaya, A. Figueiredo, A. Jain, H. Zhang, "Image classification for content-based indexing", IEEE Transactions on Image Processing, Vol. 10, pp. 117–129, 2001.
[9]M. Szummer and R. Picard., "Indoor-outdoor image classification", IEEE International Workshop on Content-based Access of Image and Video Databases, '98, Bombay, India, 1998.
[10]E.C.Yiu, "Image classification using color cues and texture orientation", Master's Thesis, Department of EECS, MIT (1996).
[11]I. Biederman, "On the semantics of a glance at a scene", in Perceptual Organizations, M. Kubovy and J. R. Pomerantz, Eds. Hillsdale, NJ: Lawrence Erlbaum, (1981), pp. 213–253.
[12]P. G. Schyns and A. Oliva, "From blobs to boundary edges: Evidence for time and spatial scale dependent scene recognition", Psychol. Sci., Vol. 5, (1994), pp. 195–200.
[13]E. Chang, K. Goh , G. Sychay, G. WU , CBSA, "Content – based soft annotation for multimodal image retrieval using bayes point machines", IEEE Transactions on Circuits and Systems for Video Technology Special Issue on Conceptual and Dynamical Aspects of Multimedia Content Description, Vol. 13 (1) (2003), pp 26–38.
[14]Gupta, D., Singh, A.K., Kumari, D., Raina, "Hybrid feature based natural scene classification using neural network", International Journal of Computer Applications, (2012), pp 48-52, 41 (16).
[15]A. Oliva, A. Torralba, "Modeling the shape of the scene: a holistic representation of the spatial envelope", International Journal of Computer Vision, Vol. 42 (3) (2001) pp 145–175.
[16]Aude Oliva, Antonio B. Torralba, Anne Guerin- Dugue and Jeanny Herault, "Global Semantic Classification of Scenes using Power Spectrum Templates ",Challenge of Image Retrieval (CIR99), Elect. Work. in Computing Series, Springer-Verlag, Newcastle, 1999.
[17]L.Fei-Fei, P. Perona, "A bayesian hierarchical model for learning natural scene categories", in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, (2005), pp. 524–531.
[18]A. Bosch, X. Munoz, A. Oliver, R Martı´, "Object and scene classification: What does a supervised approach provide us?", in: IAPR International Conference on pattern recognition, Hong Kong, 2006.
[19]R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Textural Features for Image Classification", IEEE Transactions on Systems, Man and Cybernetics, Vol. 3, no. 6,(1973) pp. 610–621.
[20]J Yu, D Tao, Y. Rui , J Cheng , "Pairwise constraints based multiview features fusion for scene classification", Pattern Recognition, Elsevier Volume 46 Issue 2, February (2013), pp. 483–496.
[21]Alessandro Perina, Marco Cristan, Vittorio Murino, "Learning natural scene classification by selective multi-scale feature extraction", Image and Vision Computing, Elsevier, Volume 28, Issue 6, June (2010), pp. 927–939.
[22]S. Grossberg.T Huang, "ARTSCENE: A neural system for natural scene classification", Journal of Vision, (2009) Volume 9(4):6, pp. 1–19.
[23]S. Belongie, C. Carson, H. Greenspan, and J. Malik, "Recognition of images in large databases using a learning framework", (1997), Technical Report No. CSD 97-939, University of Califirnia Berkeley.
[24]Patricia G. Foschi, Deepak Kolippakkam, Huan Liu and Amit Mandvikar, "Feature Extraction for Image Mining", Proceedings in International workshop on Multimedia Information System, (2002), pp. 103-109.
[25]J.C. Burges, "A tutorial on support vector machines for pattern recognition", Data Mining Knowledge Discovery 2, (1998) 1–43.
[26]S. Paek, C.L. Sable, V. Hatzivassiloglou, A. Jaimes, B.H. Schiffman, S.-F. Chang, K.R. Mckeown, "Integration of visual and text based approaches for the content labeling and classification of photographs", ACM SIGIR '99 Workshop Multimedia Indexing Retrieval, Berkeley, CA, 1999.
[27]A. Savakis, J. Luo, "Indoor vs. outdoor classification of consumer photographs", Proceedings of IEEE International Conference on Image Processing, Thessaloniki, Greece, September 2001.
[28]A. Guerin-Dugue, A. Oliva, "Classification of scene photographs from local orientation features", Pattern Recognition Letters, 21 (2000), pp 1135–1140.
[29]N. Serrano, A. Savakis, J. Luo, "A computationally efficient approach to indoor/outdoor scene classification", International Conference on Pattern Recognition (2002), QuWebec City, Canada.
[30]Matthew Boutell, Anustup Choudhury, Jiebo Luo, Christopher M. Brown, "Using semantic features for scene classification: How good do they need to be?", IEEE International Conference on Multimedia and Expo, Toronto, July 2006.
[31]Ashoka Vanjare, Omkar S. N., J.Senthilnath, "Satellite Image Processing for Land Use and Land Cover Mapping",Int. Journal of Image, Graphics and Signal Processing,2014,10,pp. 18-28.