Similar Image Retrieval Based on Grey-Level Co-Occurrence Matrix and Hu Invariants Moments Using Parallel Computing

2021 ◽  
pp. 141-155
Author(s):  
Beshaier Ali Abdulla ◽  
Yossra Hussian Ali ◽  
Nuha Jameel Ibrahim
Keyword(s):  
Parallel Computing ◽  
Image Retrieval ◽  
Grey Level ◽  
Similar Image ◽  
Occurrence Matrix

Extract the Similar Images Using the Grey Level Co-Occurrence Matrix and the Hu Invariants Moments

2020 ◽  
Vol 38 (5A) ◽  
pp. 719-727
Author(s):  
Beshaier A. Abdulla ◽  
Yossra H. Ali ◽  
Nuha J. Ibrahim
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Distance Measure ◽  
Similarity Index ◽  
Structural Similarity ◽  
Grey Level ◽  
Query Image ◽  
Image Retrieval System ◽  
Similar Images ◽  
Occurrence Matrix

In the last years, many types of research have introduced different methods and techniques for a correct and reliable image retrieval system. The goal of this paper is a comparison study between two different methods which are the Grey level co-occurrence matrix and the Hu invariants moments, and this study is done by building up an image retrieval system employing each method separately and comparing between the results. The Euclidian distance measure is used to compute the similarity between the query image and database images. Both systems are evaluated according to the measures that are used in detection, description, and matching fields which are precision, recall, and accuracy, and addition to that mean square error (MSE) and structural similarity index (SSIM) is used.  And as it shows from the results the Grey level co-occurrence matrix (GLCM) had outstanding and better results from the Hu invariants moment method.

scholarly journals A NEW DESCRIPTOR FOR IMAGE RETRIEVAL USING CONTOURLET COOCCURRENCE

2012 ◽  
Vol 15 (2) ◽  
pp. 5-16 ◽  
Author(s):  
Hoang Duc Nguyen ◽  
Thuong Tien Le ◽  
Tuan Hong Do ◽  
Cao Thu Bui
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Image Retrieval ◽  
Image Database ◽  
Experimental Results ◽  
Contourlet Transform ◽  
Grey Level ◽  
Slight Improvement ◽  
Retrieval Effectiveness ◽  
Occurrence Matrix

In this paper, a new descriptor for the feature extraction of images in the image database is presented. The new descriptor called Contourlet Co-Occurrence is based on a combination of contourlet transform and Grey Level Co-occurrence Matrix (GLCM). In order to evaluate the proposed descriptor, we perform the comparative analysis of existing methods such as Contourlet [2], GLCM [14] descriptors with Contourlet Co-Occurrence descriptor for image retrieval. Experimental results demonstrate that the proposed method shows a slight improvement in the retrieval effectiveness.

An Image Retrieval Algorithm Base on Texture Features

2014 ◽  
Vol 668-669 ◽  
pp. 1041-1044
Author(s):  
Lin Lin Song ◽  
Qing Hu Wang ◽  
Zhi Li Pei
Keyword(s):  
Image Retrieval ◽  
Statistical Methods ◽  
Texture Features ◽  
Retrieval Algorithm ◽  
Occurrence Matrix ◽  
Algorithm Base

This paper firstly studies the texture features. We construct a gray-difference primitive co-occurrence matrix to extract texture features by combining statistical methods with structural ones. The experiment results show that the features of the gray-difference primitive co-occurrence matrix are more delicate than the traditional gray co-occurrence matrix.

Modified Color Co-occurrence Matrix for Image Retrieval

2005 ◽  
pp. 43-50 ◽  
Author(s):  
Min Hyuk Chang ◽  
Jae Young Pyun ◽  
Muhammad Bilal Ahmad ◽  
Jong Hoon Chun ◽  
Jong An Park
Keyword(s):  
Image Retrieval ◽  
Occurrence Matrix

Image retrieval based on the texton co-occurrence matrix

2008 ◽  
Vol 41 (12) ◽  
pp. 3521-3527 ◽  
Author(s):  
Guang-Hai Liu ◽  
Jing-Yu Yang
Keyword(s):  
Image Retrieval ◽  
Occurrence Matrix

scholarly journals Skin Disease Recognition Method Based on Image Color and Texture Features

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Li-sheng Wei ◽  
Quan Gan ◽  
Tao Ji
Keyword(s):  
Skin Disease ◽  
Skin Diseases ◽  
Texture Features ◽  
Support Vector ◽  
Grey Level ◽  
Recognition Method ◽  
Svm Classification ◽  
Color Features ◽  
Automatic Methods ◽  
Occurrence Matrix

Skin diseases have a serious impact on people’s life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document