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]  A. Murat Tekalp,et al.  Integration of color, edge, shape, and texture features for automatic region-based image annotation and retrieval , 1998, J. Electronic Imaging.

[2]  Miroslaw Bober,et al.  Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization , 2011, Computational Imaging and Vision.

[3]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[4]  Rosalind W. Picard,et al.  Texture orientation for sorting photos "at a glance" , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[5]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[6]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[7]  Paul H. Lewis,et al.  A Fully Unsupervised Texture Segmentation Algorithm , 2003, BMVC.

[8]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Sameer Singh,et al.  Indoor vs. outdoor scene classification in digital photographs , 2005, Pattern Recognit..

[10]  J. K. AggarwalComputer.  Image Retrieval via Isotropic and Anisotropic Mappings , 2001 .

[11]  Sukhendu Das,et al.  Unsupervised Segmentation of Texture Images Using a Combination of Gabor and Wavelet Features , 2004, ICVGIP.

[12]  Jake K. Aggarwal,et al.  Applying perceptual grouping to content-based image retrieval: building images , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[13]  David G. Stork,et al.  Pattern Classification , 1973 .

[14]  Jitendra Malik,et al.  Blobworld: A System for Region-Based Image Indexing and Retrieval , 1999, VISUAL.

[15]  Jiebo Luo,et al.  Natural scene classification using overcomplete ICA , 2005, Pattern Recognit..

[16]  Z. Ling,et al.  Texture segmentation using hierarchical wavelet decomposition , 1995, 1995 Proceedings of the IEEE International Symposium on Industrial Electronics.

[17]  W. Eric L. Grimson,et al.  Combining Configurational and Statistical Approaches in Image Retrieval , 2001, IEEE Pacific Rim Conference on Multimedia.

[18]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[19]  I. Gordon.  Theories of Visual Perception , 1989 .

[20]  Chun-Shien Lu,et al.  Unsupervised texture segmentation via wavelet transform , 1997, Pattern Recognit..

[21]  Anil K. Jain,et al.  On image classification: city images vs. landscapes , 1998, Pattern Recognit..

[22]  Jiebo Luo,et al.  A computationally efficient approach to indoor/outdoor scene classification , 2002, Object recognition supported by user interaction for service robots.