Image retrieval system using R-tree self-organizing map

2007 ◽  
Vol 61 (3) ◽  
pp. 524-539 ◽  
Author(s):  
V.P. Subramanyam Rallabandi ◽  
S.K. Sett
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Self Organizing Map ◽  
Image Retrieval System ◽  
Self Organizing

Batik Image Retrieval System Using Self Organizing Map

2018 ◽  
Author(s):  
Natalia Natalia ◽  
Janson Hendryli ◽  
Dyah Erny Herwindiati
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Self Organizing Map ◽  
Image Retrieval System ◽  
Self Organizing

A Content-Based Image Retrieval System for Diagnosis and Detection of Skin Cancer Using Self-Organizing Feature Maps

2021 ◽  
pp. 11-36
Author(s):  
K. Sujatha ◽  
R.S. Ponmagal ◽  
Rajeswari Hari ◽  
M. Anand ◽  
V. Karthikeyan ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Image Retrieval ◽  
Retrieval System ◽  
Content Based Image Retrieval ◽  
Feature Maps ◽  
Image Retrieval System ◽  
Self Organizing

scholarly journals Content-based image retrieval using a signature graph and a self-organizing map

2016 ◽  
Vol 26 (2) ◽  
pp. 423-438 ◽  
Author(s):  
Thanh The Van ◽  
Thanh Manh Le
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Discrete Wavelet ◽  
Self Organizing Map ◽  
Segmentation Method ◽  
Image Retrieval System ◽  
Similar Images ◽  
Wavelet Frame Transform ◽  
Image Object ◽  
Self Organizing

Abstract In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL,Wang and MSRDI.

scholarly journals Clustering of Digital Images using Shape Features with SOM

2019 ◽  
Vol 8 (12S) ◽  
pp. 918-922
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Digital Images ◽  
Research Work ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Content Based Image Retrieval ◽  
Self Organizing Map ◽  
Shape Features ◽  
Image Retrieval System

In these days people are interested in using digital images. So the size of image databases is increasing rapidly. It leads retrieval problem of images from large databases. Machine learning algorithms are applying in recent research to simplify the task of image retrieval and make it automatic. Thus the concept of content based image retrieval system came into existence. In this system the images are extracted based on similar content. Content means features of the images and it is formed by feature extraction of the images in databases. Contents can be edges, color, shape, gradient, orientation, histogram gradient etc. These contents are clustered into various groups of similar feature vectors. So for any input image the selected feature is searched for and image is retrieved from the database. This reduces the time complexity. There have been many algorithms for implementing the content based image retrieval system. In this research work we propose a novel paradigm where in shape features are extracted from the database images and are used to train the self-organizing map to cluster the shape features. These clusters are then used for image retrieval.

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