A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Retrieval Performance Improvement through Low Rank Corrections
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
International Journal of Robotics Research
On Low Distortion Embeddings of Statistical Distance Measures into Low Dimensional Spaces
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Audio query by example using similarity measures between probability density functions of features
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on scalable audio-content analysis
Region-based image retrieval using the semantic cluster matrix and adaptive learning
International Journal of Computational Science and Engineering
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When the organization of images in a database is well described with pre-defined semantic categories, it can be useful for category specific searching. In this work, we investigate a supervised learning approach to associate low-dimensional image features with their high level semantic categories and utilize the category specific feature distribution information in statistical similarity matching. A multi-class support vector classifier (SVC) is trained to predict the categories of query and database images. Based on the online prediction, pre-computed category specific first and second order statistical parameters are utilized in similarity measure functions on the assumption that, distributions are multivariate Gaussian. A high dimensional feature vector would increase the computational complexity, logical database size and moreover, incorporate inaccuracy in parameter estimation. We also propose a fusion (early, late, and no fusion) based principal component analysis (PCA) to reduce the dimensionality based on both independent and dependent assumptions of image features. Experimental results on the reduced feature dimensions are reported on a generic image database with ground-truth or known categories. Performances of two statistical distance measures (e.g., Bhattacharyya & Mahalanobis) are evaluated and compared with commonly used Euclidean distance, which show the effectiveness of the proposed technique.