An Efficient Image Clustering and Content Based Image Retrieval Using Fuzzy K Means Clustering Algorithm

2014 ◽  
Vol 9 (1) ◽  
pp. 147 ◽  
Author(s):  
K. Haridas ◽  
Antony Selvadoss Thanamani
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Content Based Image Retrieval ◽  
Image Clustering

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.

FUZZY ART-BASED IMAGE CLUSTERING METHOD FOR CONTENT-BASED IMAGE RETRIEVAL

2007 ◽  
Vol 06 (02) ◽  
pp. 213-233 ◽  
Author(s):  
SANG-SUNG PARK ◽  
KWANG-KYU SEO ◽  
DONG-SIK JANG
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Image Clustering ◽  
Clustering Method ◽  
Image Region ◽  
Shape Information ◽  
Fuzzy Art ◽  
The Neural Network ◽  
Joint Histogram ◽  
Noise Input

In this paper, an image clustering method that is essential for content-based image retrieval in large image databases efficiently is proposed by color, texture, and shape contents. The dominant triple HSV (Hue, Saturation, and Value), which are extracted from quantized HSV joint histogram in the image region, are used for representing color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Due to its algorithmic simplicity and the several merits that facilitate the implementation of the neural network, Fuzzy ART has been exploited for image clustering. Original Fuzzy ART suffers unnecessary increase of the number of output neurons when the noise input is presented. Therefore, the improved Fuzzy ART algorithm is proposed to resolve the problem by differently updating the committed node and uncommitted node, and checking the vigilance test again. To show the validity of the proposed algorithm, experimental results on image clustering performance and comparison with original Fuzzy ART are presented in terms of recall rates.

scholarly journals Image Retrieval Based on Content Using Color Feature

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmed J. Afifi ◽  
Wesam M. Ashour
Keyword(s):  
Image Retrieval ◽  
Image Enhancement ◽  
Clustering Algorithm ◽  
Content Based Image Retrieval ◽  
Visual Feature ◽  
Color Feature ◽  
Retrieval Time ◽  
Speed Up

Content-based image retrieval from large resources has become an area of wide interest in many applications. In this paper we present a CBIR system that uses Ranklet Transform and the color feature as a visual feature to represent the images. Ranklet Transform is proposed as a preprocessing step to make the image invariant to rotation and any image enhancement operations. To speed up the retrieval time, images are clustered according to their features using k-means clustering algorithm.

scholarly journals Feature Fusion for Image Retrieval using Image Processing

2019 ◽  
Vol 8 (12) ◽  
pp. 3157-3162
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Retrieval ◽  
Principle Component Analysis ◽  
Clustering Algorithm ◽  
Feature Fusion ◽  
The Other ◽  
Content Based Image Retrieval ◽  
Huge Amount ◽  
Attribute Data

Image processing and computer vision uses Content-based image retrieval (CBIR) function to solve the issue of image retrieval, which means, solving the issue of image searching in expansive databases. The actual data of the image will be evaluated when a search is performed that refers to content-based. The term content can be any attribute of an image like colour-shade, various symbols or shapes, sizes, or any other data. There are various approaches for image retrieval but the most prominent are by comparing the main image with the subsets of the relatable images whether it matches or not and the other one is by using a matching descriptor for the image. One of the main trouble for huge amount of CBIR is the representation of an image. When a given image is worked upon it is divided into number of attributes in which some are the primary ones and others are the secondary ones. These attributes are checked with the local and MPEG-7 descriptors. All this is then mapped in a single vector which is the same images but in compact form to save the space. Principle Component Analysis (PCA) is used lessen the attribute size. To store the attribute data in similar clusters and to train them to give the correct output the study also uses k-means clustering algorithm. Hence, the proposed system deals with the image retrieval using various algorithms and methods.

Content-based image retrieval using PSO and k-means clustering algorithm

2014 ◽  
Vol 8 (8) ◽  
pp. 6211-6224 ◽  
Author(s):  
Zeyad Safaa Younus ◽  
Dzulkifli Mohamad ◽  
Tanzila Saba ◽  
Mohammed Hazim Alkawaz ◽  
Amjad Rehman ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Content Based Image Retrieval
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