Neural Networks
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer and Robot Vision
Image Retrieval Via Isotropic and Anisotropic Mappings
PRIS '01 Proceedings of the 1st International Workshop on Pattern Recognition in Information Systems: In conjunction with ICEIS 2001
Combining Configurational and Statistical Approaches in Image Retrieval
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A Computationally Efficient Approach to Indoor/Outdoor Scene Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization
Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Texture segmentation using hierarchical wavelet decomposition
Pattern Recognition
Natural scene classification using overcomplete ICA
Pattern Recognition
Indoor vs. outdoor scene classification in digital photographs
Pattern Recognition
A Novel Method for Efficient Indoor---Outdoor Image Classification
Journal of Signal Processing Systems
Exploiting depth information for indoor-outdoor scene classification
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Boosting k-NN for Categorization of Natural Scenes
International Journal of Computer Vision
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We propose a method for indoor versus outdoor scene classification using a probabilistic neural network (PNN). The scene is initially segmented (unsupervised) using fuzzy C-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.