Wavelet Domain Distributed Information Entropy and Genetic Clustering Algorithm for Image Retrieval

2012 ◽  
pp. 87-94
Author(s):  
Kamil Moydin ◽  
Askar Hamdulla
Keyword(s):  
Image Retrieval ◽  
Information Entropy ◽  
Clustering Algorithm ◽  
Wavelet Domain ◽  
Distributed Information ◽  
Genetic Clustering

CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM

2007 ◽  
Vol 16 (06) ◽  
pp. 919-934
Author(s):  
YONGGUO LIU ◽  
XIAORONG PU ◽  
YIDONG SHEN ◽  
ZHANG YI ◽  
XIAOFENG LIAO
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Sum Of Squares ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Mutation Operation ◽  
Iteration Methods ◽  
Genetic Clustering ◽  
The Individual

In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.

scholarly journals Image Retrieval Using Data Mining Technique

2020 ◽  
pp. 2115-2125
Author(s):  
Sarmad T. Abdul-Samad ◽  
Sawsan Kamal
Keyword(s):  
Image Retrieval ◽  
User Satisfaction ◽  
Clustering Algorithm ◽  
Retrieval System ◽  
Spatial Information ◽  
Search Space ◽  
Extraction Process ◽  
Retrieval Process ◽  
Data Mining Technique ◽  
Image Retrieval System

Even though image retrieval is considered as one of the most important research areas in the last two decades, there is still room for improvement since it is still not satisfying for many users. Two of the major problems which need to be improved are the accuracy and the speed of the image retrieval system, in order to achieve user satisfaction and also to make the image retrieval system suitable for all platforms. In this work, the proposed retrieval system uses features with spatial information to analyze the visual content of the image. Then, the feature extraction process is followed by applying the fuzzy c-means (FCM) clustering algorithm to reduce the search space and speed up the retrieval process. The experimental results show that using the spatial features increases the system accuracy and that the clustering algorithm speeds up the image retrieval process. This shows that the proposed system works with texture and non-texture images.  

scholarly journals Statistical Model Based Digital Image Retrieval and Fragile Watermarking

2021 ◽  
Author(s):  
Hua Yuan
Keyword(s):  
Image Retrieval ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Statistical Modelling ◽  
Gaussian Mixture ◽  
Image Features ◽  
Fragile Watermarking ◽  
Model Parameters ◽  
Wavelet Domain ◽  
Watermarking Scheme

The objective of this thesis is to acquire abstract image features through statistical modelling in the wavelet domain and then based on the extracted image features, develop an effective content-based image retreival (CBIR) system and a fragile watermarking scheme. In this thesis, we first present a statistical modelling of images in the wavelet domain through a Gaussian mixture model (GMM) and a generalized Gaussian mixture model (GGMM). An Expectation Maximization (EM) algorithm is developed to help estimate the model parameters. A novel similarity measure based on the Kullback-Leibler divergence is also developed to calculate the distance of two distinct model distributions. We then apply the statistical modelling to two application areas: image retrieval and fragile watermarking. In image retrieval, the model parameters are employed as image features to compose the indexing feature space, while the feature distance of two compared images is computed using the novel similarity measure. The new image retrieval method has a better retrieval performance than most conventional methods. In fragile watermarking, the model parameters are utilized for the watermark embedding. The new watermarking scheme achieves a virtually imperceptible embedding of watermarks because it modifies only a few image data and embeds watermarks at image texture edges. A multiscale embedding of fragile watermarks is given to enhance the embeddability rate and on the other hand, to constitute a semi-fragile approach.

scholarly journals An Integrated Method for Improving Remodularization in Software Systems using Probability Method

2019 ◽  
Vol 8 (9) ◽  
pp. 2662-2668
Keyword(s):  
Clustering Algorithm ◽  
Software Systems ◽  
Probability Method ◽  
Software System ◽  
Integrated Method ◽  
Software Modularization ◽  
Estimation Of Distribution ◽  
Automatic Software ◽  
Structural Connections ◽  
Genetic Clustering

Effective software system must advance to stay pertinent, however this procedure of development can cause the product design to rot and prompt essentially diminished efficiency and even dropped projects. Remodularization tasks can be performed to fix the structure of a software system and evacuate the disintegration brought about by programming advancement. Software remodularization comprises in rearranging software entities into modules to such an extent that sets of substances having a place with similar modules are more comparable than those having a place with various modules.However, re-modularizing systems automatically is challenging in order to enhance their sustainability. In this paper, we have introduced a procedure of automatic software remodularization that helps software maintainers to enhance the software modularization quality by assessing the coupling and attachment among programming components. For precision coupling measures, the proposed technology uses structural coupling measurements. The proposed methodology utilizes tallying of class' part capacities utilized by a given class as a basic coupling measure among classes. The interaction between class files measures structural connections between software elements (classes). In this paper, probability based remodularization (PBR) approach has been proposed to remodularize the software systems. The file ordering process is done by performing probability based approach and remodularization is done based on the dependency strength or connectivity among the files. The proposed technique is experimented on seven software systems. The efficiency is measured by utilizing Turbo Modularization Quality (MQ) that promotes edge weighing module dependence graph (MDG). It very well may be presumed that when comparing performance with the subsisting techniques, for instance, Bunch – GA (Genetic Algorithm), DAGC (Development of Genetic Clustering Algorithm) and Estimation of Distribution Algorithm (EDA), the proposed methodology has greater Turbo MQ value and lesser time complexity with Bunch-GA in the software systems assessed

An Image Clustering and Feedback-Based Retrieval Framework

2012 ◽  
pp. 62-80
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Image Region ◽  
Retrieval Systems ◽  
Object Based ◽  
Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

An Image Clustering and Feedback-based Retrieval Framework

2010 ◽  
Vol 1 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Single Object ◽  
Image Region ◽  
Retrieval Accuracy ◽  
Retrieval Systems ◽  
Object Based ◽  
Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

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