Image Recognition Based on Shape and Texture Features

2014 ◽  
Vol 536-537 ◽  
pp. 127-130
Author(s):  
Kun Geng
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Retrieval System ◽  
Texture Features ◽  
Morphological Processing ◽  
Content Based Image Retrieval ◽  
Shape Features ◽  
Key Technologies ◽  
Image Retrieval System ◽  
Important Position

Based on the shape of the image retrieval occupy an important position in the content-based image retrieval, and studied architecture, content-based image retrieval system, ie research-based image retrieval key technologies shape features for image noise in addition to the morphological processing; image segmentation; shape-based feature extraction and regional boundaries and description techniques and similarity measure techniques. The results show that the algorithm can effectively identify the characteristics of the image.

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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