scholarly journals Contribution-based clustering algorithm for content-based image retrieval

2010 ◽  
Author(s):  
Harikrishna Narasimhan ◽  
Purushothaman Ramraj
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Content Based Image Retrieval

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

scholarly journals Image Retrieval Based on WBCH and Clustering Algorithm

2013 ◽  
Vol 5 (3) ◽  
pp. 604-613
Author(s):  
Asmita Bhaskar Shirsath ◽  
M. J. Chouhan ◽  
N. J Uke
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Similarity Measures ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Shape Information ◽  
Image Descriptors ◽  
Image Retrieval System ◽  
Retrieval Result

Research on content-based image retrieval has gained tremendous momentum during the last decade. Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. In order to get faster  retrieval result from large-scale image database ,we proposed image retrieval system in which image database is first pre-processed by Wavelet Based Color Histogram (WBCH) and K-means algorithm and then using Hierarchical clustering algorithm we index the previous result and then by using similarity measures we retrieve the images from pre-processed database. Experiments show that this proposed method offers substantial increase in retrieval speed but needs to be improved on retrieval results.

scholarly journals A Content-Based Image Retrieval Method By Exploiting Cluster Shapes

2018 ◽  
Vol 14 (2) ◽  
pp. 90-102 ◽  
Author(s):  
Hanan Al-Jubouri ◽  
Hongbo Du
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Texture Features ◽  
Gaussian Mixture ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Automatic Process ◽  
Query Image ◽  
Number Of Clusters ◽  
Leibler Divergence

Content-Based Image Retrieval (CBIR) is an automatic process of retrieving images that are the most similar to a query image based on their visual content such as colour and texture features. However, CBIR faces the technical challenge known as the semantic gap between high level conceptual meaning and the low-level image based features. This paper presents a new method that addresses the semantic gap issue by exploiting cluster shapes. The method first extracts local colours and textures using Discrete Cosine Transform (DCT) coefficients. The Expectation-Maximization Gaussian Mixture Model (EM/GMM) clustering algorithm is then applied to the local feature vectors to obtain clusters of various shapes. To compare dissimilarity between two images, the method uses a dissimilarity measure based on the principle of Kullback-Leibler divergence to compare pair-wise dissimilarity of cluster shapes. The paper further investigates two respective scenarios when the number of clusters is fixed and adaptively determined according to cluster quality. Experiments are conducted on publicly available WANG and Caltech6 databases. The results demonstrate that the proposed retrieval mechanism based on cluster shapes increases the image discrimination, and when the number of clusters is fixed to a large number, the precision of image retrieval is better than that when the relatively small number of clusters is adaptively determined.

scholarly journals Modified cuckoo search algorithm: Feature subset selection &Shape,Color and Texture Features Descriptors for Content-Based Image Retrieval

2021 ◽  
Vol 23 (12) ◽  
pp. 525-541
Author(s):  
Mrs.K. Radha ◽  
◽  
Mrs. . R.V.Sudha ◽  
Mrs.M. Meena ◽  
Dr.R. Jayavadivel ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Fitness Function ◽  
Texture Features ◽  
Cuckoo Search ◽  
Detection Algorithm ◽  
Feature Subset Selection ◽  
Content Based Image Retrieval ◽  
Feature Subset

With the recent advances in knowledge, the complication of multimedia has increased expressively and new areas of research have opened up in search of new multimedia content. Content-based image retrieval (CBIR) are used to extract images associated with image queries (IQs) from huge databases. The CBIR schemes accessible at present have limited functionality because they only have a partial number of functions. This document presents an improved cookie detection algorithm with coarse sentences for processing large amounts of data using selected examples. The improved cuckoo detection algorithm mimics the behavior of brood attachment parasites in some cuckoo species, including some birds. Modified cuckoo recognition uses approximate set theory to create a fitness function that takes into account the sum of features and the quality of classification as a small amount. For an image entered as IQ from a database, distance metrics are used to find the appropriate image. This is the central idea of CBIR. The projected CBIR method is labelled and can extract shape features based on the RGB color using the and canny Edge (CED) and neutrosophic clustering algorithm scheme. After YCbCrcolor cut, and the CED to get the features to extract the vascular matrix. The combination of these techniques improves the efficiency of the CBR image recovery infrastructure. In this thesis recursive neural network techniques are used to measure the similarity. In addition, the accuracy of the results is: The recall score is measured to evaluate system performance. The proposed CBIR system provides more precise and accurate values than the complex CBIR system.

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