Selecting a discriminant subset of co-occurrence matrix features for texture-based image retrieval

Authors:
Najlae Idrissi;José Martinez;Driss Aboutajdine
Affiliations:
Atlas-GRIM, INRIA & LINA (FRE CNRS 2729) Polytechnic School of the University of Nantes, Nantes, France;Atlas-GRIM, INRIA & LINA (FRE CNRS 2729) Polytechnic School of the University of Nantes, Nantes, France;GSCM Science Faculty of Rabat, University Mohamed V Rabat, Morocco
Venue:
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Year:
2005

Citing 10
Cited 1

Texture feature performance for image segmentation

Pattern Recognition
The R*-tree: an efficient and robust access method for points and rectangles

SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Color indexing

International Journal of Computer Vision
The SR-tree: an index structure for high-dimensional nearest neighbor queries

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Extracting periodicity of a regular texture based on autocorrelation functions

Pattern Recognition Letters
Filtering for Texture Classification: A Comparative Study

IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence
R-trees: a dynamic index structure for spatial searching

SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The X-tree: An Index Structure for High-Dimensional Data

VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
NeTra: a toolbox for navigating large image databases

ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1

Feature Selection for Neural-Network Based No-Reference Video Quality Assessment

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II

Quantified Score

Hi-index	0.01

Visualization

Abstract

In the general case, searching for images in a content-based image retrieval (CBIR) system amounts essentially, and unfortunately, to a sequential scan of the whole database. In order to accelerate this process, we want to generate summaries of the image database. In this paper, we focus on the selection of the texture features that will be used as a signature in our forthcoming system. We analysed the descriptors extracted from grey-level co-occurrence matrices’s (COM) under the constraints imposed by database systems.