Handbook of pattern recognition & computer vision
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual information retrieval
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Estimation and choice of neighbors in spatial-interaction models of images
IEEE Transactions on Information Theory
Multiscale Texture Extraction with Hierarchical (BV,Gp,L2) Decomposition
Journal of Mathematical Imaging and Vision
Noise robust rotation invariant features for texture classification
Pattern Recognition
Hi-index | 0.00 |
In this paper we propose to revisit the well-known autoregressive model (AR) as a texture representation model. We consider the AR model with causal neighborhoods. First, we will define the AR model and discuss briefly the parameters estimation process. Then, we will present the synthesis algorithm and we will show some experimental results. A perceptual interpretation of the AR estimated parameters will be then proposed and discussed. In particular, a computational measure to estimate the degree of randomness/regularity of textures is proposed. The set of the estimated parameters will be then applied in content-based image retrieval (CBIR) to model texture content and experimental results are shown. Benchmarking, using the precision/recall measures conducted on the well-known Brodatz database, shows interesting results.