An efficient content-based image retrieval with ant colony optimization feature selection schema based on wavelet and color features

2015 ◽  
Author(s):  
Abdolreza Rashno ◽  
Saeed Sadri ◽  
Hossein SadeghianNejad
Keyword(s):  
Feature Selection ◽  
Image Retrieval ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Content Based Image Retrieval ◽  
Color Features

scholarly journals A Performance Evaluation of Shape Based Image Retrieval Using Heuristic Function

2013 ◽  
Vol 12 (2) ◽  
pp. 3241-3248
Author(s):  
Parmalik Kumar ◽  
Pushpa Tandekar ◽  
Dhirendra Kumar Jha
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Image Retrieval ◽  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Ant Colony ◽  
Content Based Image Retrieval ◽  
Research Trend ◽  
Shape Feature ◽  
Heuristic Function

Heuristic function plays an important role in content based image retrieval. The heuristic function used for feature selection and feature optimization for retrieval process. The feature selection process are depends on feature extraction process. The content based image consists of three types of features such as color, texture and shape. The shape feature is very important feature for image retrieval. The extraction of shape feature various authors used different method such ad Gabor filter, wavelet transform function and Fourier descriptor. Now in current research trend MPEG-7 feature descriptor are mostly authors are used. In this paper descried the review of content based image retrieval based on shape based feature and optimization technique such as ANT colony optimization, genetic algorithm and neural network. The empirical evaluation result shows that ANT colony optimization technique is better optimization technique in compared with other such as genetic and neural network.

Ant Colony Optimization for Use in Content Based Image Retrieval

2011 ◽  
pp. 384-404
Author(s):  
Konstantinos Konstantinidis ◽  
Georgios Ch. Sirakoulis ◽  
Ioannis Andreadis
Keyword(s):  
Image Retrieval ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Spatial Information ◽  
Ant Colony ◽  
Content Based Image Retrieval ◽  
Computational Burden ◽  
Whole Process ◽  
Histogram Intersection ◽  
Speed Up

The aim of this chapter is to provide the reader with a Content Based Image Retrieval (CBIR) system which incorporates AI through ant colony optimization and fuzzy logic. This method utilizes a two-stage fuzzy modified ant colony algorithm employing in parallel low-level features such as color, texture and spatial information which are extracted from the images themselves. The results prove the system to be more efficient compared to popular and contemporary methods such as the histogram intersection, joint histograms and the scalable color histogram of the MPEG-7 standard. However, due to the high computational burden of the AI methods the system is quite slow when implemented in software. Thus in order to speed up the whole process the reader is also provided with the hardware implementation analysis of the whole system. The increase in speed is phenomenal.

scholarly journals Learning effective color features for content based image retrieval in dermatology

2011 ◽  
Vol 44 (9) ◽  
pp. 1892-1902 ◽  
Author(s):  
Kerstin Bunte ◽  
Michael Biehl ◽  
Marcel F. Jonkman ◽  
Nicolai Petkov
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Color Features
Sign in / Sign up

Export Citation Format

Share Document