Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
A Sparse Support Vector Machine Approach to Region-Based Image Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Image classification for content-based indexing
IEEE Transactions on Image Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Classification improvement of local feature vectors over the KNN algorithm
Multimedia Tools and Applications
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In this paper we address the task of image categorization using a new similarity measure on the space of Sparse Multiscale Patches (SMP ). SMP s are based on a multiscale transform of the image and provide a global representation of its content. At each scale, the probability density function (pdf ) of the SMP s is used as a description of the relevant information. The closeness between two images is defined as a combination of Kullback-Leibler divergences between the pdfs of their SMP s. In the context of image categorization, we represent semantic categories by prototype images, which are defined as the centroids of the training clusters. Therefore any unlabeled image is classified by giving it the same label as the nearest prototype. Results obtained on ten categories from the Corel collection show the categorization accuracy of the SMP method.