A Performance Evaluation of Shape Based Image Retrieval Using 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.

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

2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP) ◽

10.1109/aisp.2015.7123522 ◽

2015 ◽

Cited By ~ 15

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

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Content-Based Image Retrieval Using Color and Texture Features Through Ant Colony Optimization

Advances in Intelligent Systems and Computing - Computing, Communication and Signal Processing ◽

10.1007/978-981-13-1513-8_104 ◽

2018 ◽

pp. 1029-1037

Author(s):

Nitin Jain ◽

S. S. Salankar

Keyword(s):

Image Retrieval ◽

Ant Colony Optimization ◽

Texture Features ◽

Ant Colony ◽

Content Based Image Retrieval ◽

Color And Texture Features

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An Ant Colony Optimization Algorithm Based Image Classification Method for Content-Based Image Retrieval in Cloud Computing Environment

Communications in Computer and Information Science - Computer Applications for Web, Human Computer Interaction, Signal and Image Processing, and Pattern Recognition ◽

10.1007/978-3-642-35270-6_16 ◽

2012 ◽

pp. 110-117

Author(s):

Kwang-Kyu Seo

Keyword(s):

Cloud Computing ◽

Image Retrieval ◽

Image Classification ◽

Ant Colony Optimization ◽

Optimization Algorithm ◽

Ant Colony ◽

Content Based Image Retrieval ◽

Ant Colony Optimization Algorithm ◽

Computing Environment ◽

Cloud Computing Environment

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An optimized feature selection using fuzzy mutual information based ant colony optimization for software defect prediction

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.1.9954 ◽

2017 ◽

Vol 7 (1.1) ◽

pp. 456

Author(s):

G Manivasagam ◽

R Gunasundari

Keyword(s):

Feature Selection ◽

Software Engineering ◽

Mutual Information ◽

Ant Colony Optimization ◽

Optimization Technique ◽

Ant Colony ◽

Defect Prediction ◽

Software Defect Prediction ◽

Software Defects ◽

Software Defect

In recent years, there is a significant notification focused towards the prediction of software defect in the field of software engineering. The prediction of software defects assist in reducing the cost of testing effort, improving the process of software testing and to concentrate only on the fault-prone software modules. Recently, software defect prediction is an important research topic in the software engineering field. One of the important factors which effect the software defect detection is the presence of noisy features in the dataset. The objective of this proposed work is to contribute an optimization technique for the selection of potential features to improve the prediction capability of software defects more accurately. The Fuzzy Mutual Information Ant Colony Optimization is used for searching the optimal feature set with the ability of Meta heuristic search. This proposed feature selection efficiency is evaluated using the datasets from NASA metric data repository. Simulation results have indicated that the proposed method makes an impressive enhancement in the prediction of routine for three different classifiers used in this work.

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Ant Colony Optimization for Use in Content Based Image Retrieval

Handbook of Research on Artificial Immune Systems and Natural Computing ◽

10.4018/978-1-60566-310-4.ch018 ◽

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.

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