Abstract
We propose a method for indoor versus outdoor scene classification using a probabilistic neural network (PNN). The scene is initially segmented (unsupervised) using fuzzy-means clustering (FCM) and features based on color, texture, and shape are extracted from each of the image segments. The image is thus represented by a feature set, with a separate feature vector for each image segment. As the number of segments differs from one scene to another, the feature set representation of the scene is of varying dimension. Therefore a modified PNN is used for classifying the variable dimension feature sets. The proposed technique is evaluated on two databases: IITM-SCID2 (scene classification image database) and that used by Payne and Singh in 2005. The performance of different feature combinations is compared using the modified PNN.
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Saber E, Tekalp AM: Integration of color, edge, shape, and texture features for automatic region-based image annotation and retrieval. Journal of Electronic Imaging 1998,7(3):684–700. 10.1117/1.482605
Payne A, Singh S: Indoor vs. outdoor scene classification in digital photographs. Pattern Recognition 2005,38(10):1533–1545. 10.1016/j.patcog.2004.12.014
Jain AK, Vailaya A: Image retrieval using color and shape. Pattern Recognition 1996,29(8):1233–1244. 10.1016/0031-3203(95)00160-3
Vailaya A, Jain A, Zhang HJ: On image classification: city images vs. landscapes. Pattern Recognition 1998,31(12):1921–1935. 10.1016/S0031-3203(98)00079-X
Iqbal Q, Aggarwal JK: Image retrieval via isotropic and anisotropic mappings. Proceedings of IAPR Workshop on Pattern Recognition in Information Systems, July 2001, Setubal, Portugal 34–49.
Iqbal Q, Aggarwal JK: Applying perceptual grouping to content-based image retrieval: building images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), June 1999, Fort Collins, Colo, USA 1: 42–48.
Haralick RM, Shapiro LG: Computer and Robot Vision. Addison-Wesley, Reading, Mass, USA; 1992.
Yu H, Grimson WEL: Combining configurational and statistical approaches in image retrieval. Proceedings of the 2nd IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing, October 2001, Beijing, China, Lecture Notes in Computer Science 2195: 293–300.
Wang JZ, Li J, Wiederhold G: Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001,23(9):947–963. 10.1109/34.955109
Luo J, Boutell M: Natural scene classification using overcomplete ICA. Pattern Recognition 2005,38(10):1507–1519. 10.1016/j.patcog.2005.02.015
Gorkani MM, Picard RW: Texture orientation for sorting photos "at a glance". Proceedings of the 12th International Conference on Pattern Recognition (ICPR '94), October 1994, Jerusalem, Israel 1: 459–464.
Navid Serrano AS, Luo J: A computationally efficient approach to indoor/outdoor scene classification. Proceedings of the International Conference on Pattern Recognition (ICPR '02), August 2002, Quebec City, Quebec, Canada 4: 146–149.
Rao SG, Puri M, Das S: Unsupervised segmentation of texture images using a combination of gabor and wavelet features. Proceedings of the 4th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP '04), December 2004, Kolkata, India 370–375.
Fauzi MFA, Lewis PH: A fully unsupervised texture segmentation algorithm. Proceedings of the British Machine Vision Conference (BMVC '03), September 2003, Norwich, UK 519–528.
Salari E, Ling Z: Texture segmentation using hierarchical wavelet decomposition. Pattern Recognition 1995, 28: 1819–1824. 10.1016/0031-3203(95)00054-2
Gordon IE: Theories of Visual Perception. 3rd edition. Psychology Press, New York, NY, USA; 2004.
Lu C-S, Chung P-C, Chen C-F: Unsupervised texture segmentation via wavelet transform. Pattern Recognition 1997,30(5):729–742. 10.1016/S0031-3203(96)00116-1
Carson C, Thomas M, Belongie M, Hellerstein J, Malik J: Blobworld: a system for region based image indexing and retrieval. Proceedings of the 3rd International Conference on Visual Information Systems, June 1999, Amsterdam, The Netherlands
Mokhtarian F, Bober M: Curvature Scale Space Representation: Theory, Applications and MPEG-7 Standarization. Kluwer Academic, Boston, Mass, USA; 2003.
Specht DF: Probabilistic neural networks. Neural Networks 1990,3(1):109–118. 10.1016/0893-6080(90)90049-Q
Richard PEH, Duda O, Stork DG: Pattern Classification. John Wiley & Sons, New York, NY, USA; 2004.
IIT Madras Scene Classification Image Database (SCID) https://doi.org/vplab.cs.iitm.ernet.in/SCID/
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Gupta, L., Pathangay, V., Patra, A. et al. Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network. EURASIP J. Adv. Signal Process. 2007, 094298 (2006). https://doi.org/10.1155/2007/94298
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DOI: https://doi.org/10.1155/2007/94298