Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A society of models for video and image libraries
IBM Systems Journal
Time, relevance and interaction modelling for information retrieval
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Visual information retrieval from large distributed online repositories
Communications of the ACM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual information retrieval
Computer Analysis of Visual Textures
Computer Analysis of Visual Textures
Autocovariance-based Perceptual Textural Features Corresponding to Human Visual Perception
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Information retrieval from visual databases using multiple representations and multiple queries
Proceedings of the 2009 ACM symposium on Applied Computing
An approach based on multiple representations and multiple queries for invariant image retrieval
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
VISUAL'05 Proceedings of the 8th international conference on Visual Information and Information Systems
Content-based retrieval and classification of ultrasound medical images of ovarian cysts
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
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This paper addresses the fundamental issues of visual content representation and similarity matching in content-based image retrieval and image databases in general. Simply stated, defining an image retrieval system is equivalent to find answers to two fundamental questions: 1. Representation model or which features are used to represent the content of images; 2. Once the set of features representing the content of images is determined, the question of how to combine the individual or partial similarities according to each feature to form a global similarity must be addressed. In this paper, a new similarity model is introduced based on the Gower coefficient of similarity. This similarity model is flexible and can be declined in several versions: non-weighted, weighted and hierarchical versions. This model was applied to a sample of homogeneous textured images considering two representation models: the autoregressive model, a purely statistical model, and an empirical perceptual model based on perceptual features such as coarseness and directionality. Experimentations show very interesting results.