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Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network


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.


  1. 1.

    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

    Article  Google Scholar 

  2. 2.

    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

    Article  Google Scholar 

  3. 3.

    Jain AK, Vailaya A: Image retrieval using color and shape. Pattern Recognition 1996,29(8):1233–1244. 10.1016/0031-3203(95)00160-3

    Article  Google Scholar 

  4. 4.

    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

    Article  Google Scholar 

  5. 5.

    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.

    Google Scholar 

  6. 6.

    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.

    Article  Google Scholar 

  7. 7.

    Haralick RM, Shapiro LG: Computer and Robot Vision. Addison-Wesley, Reading, Mass, USA; 1992.

    Google Scholar 

  8. 8.

    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.

    MATH  Google Scholar 

  9. 9.

    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

    Article  Google Scholar 

  10. 10.

    Luo J, Boutell M: Natural scene classification using overcomplete ICA. Pattern Recognition 2005,38(10):1507–1519. 10.1016/j.patcog.2005.02.015

    Article  Google Scholar 

  11. 11.

    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.

    Article  Google Scholar 

  12. 12.

    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.

    Google Scholar 

  13. 13.

    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.

    Google Scholar 

  14. 14.

    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.

    Google Scholar 

  15. 15.

    Salari E, Ling Z: Texture segmentation using hierarchical wavelet decomposition. Pattern Recognition 1995, 28: 1819–1824. 10.1016/0031-3203(95)00054-2

    Article  Google Scholar 

  16. 16.

    Gordon IE: Theories of Visual Perception. 3rd edition. Psychology Press, New York, NY, USA; 2004.

    Google Scholar 

  17. 17.

    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

    Article  Google Scholar 

  18. 18.

    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

    Google Scholar 

  19. 19.

    Mokhtarian F, Bober M: Curvature Scale Space Representation: Theory, Applications and MPEG-7 Standarization. Kluwer Academic, Boston, Mass, USA; 2003.

    Google Scholar 

  20. 20.

    Specht DF: Probabilistic neural networks. Neural Networks 1990,3(1):109–118. 10.1016/0893-6080(90)90049-Q

    Article  Google Scholar 

  21. 21.

    Richard PEH, Duda O, Stork DG: Pattern Classification. John Wiley & Sons, New York, NY, USA; 2004.

    Google Scholar 

  22. 22.

    IIT Madras Scene Classification Image Database (SCID)

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Correspondence to Lalit Gupta.

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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).

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  • Color
  • Neural Network
  • Information Technology
  • Feature Vector
  • Quantum Information