Authors:
Ismail Elsayad
;
Jean Martinet
;
Thierry Urruty
;
Taner Danisman
;
Haidar Sharif
and
Chabane Djeraba
Affiliation:
Lille 1 University, France
Keyword(s):
SURF, Bag-of-visual-words, Visual phrases, Gaussian mixture model, Spatial weighting.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Implementation of Image and Video Processing Systems
;
Informatics in Control, Automation and Robotics
;
Sensor Networks
;
Signal Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Statistical Approach
;
Structural and Syntactic Approach
Abstract:
Nowadays, having effective methods for accessing the desired images is essential with the huge amount of digital images. The aim of this paper is to build a meaningful mid-level representation of visual documents to be used later for matching between the query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Then, we represent the spatial constitution of an image as a mixture of n Gaussians in the feature space. Finally, we extract different association rules between frequent visual words in the local context of the image to construct visual phrases. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.