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.

Color Image Retrieval Algorithm Based on Dual Tree Complex Wavelet Transform

2013 ◽  
Vol 427-429 ◽  
pp. 1761-1764
Author(s):  
Xiao Li Zhao ◽  
Shu Jun Yin
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Color Image ◽  
Texture Features ◽  
Retrieval Algorithm ◽  
Complex Wavelet Transform ◽  
Invariant Moments ◽  
Frequency Components ◽  
Complex Wavelet ◽  
Form Feature

A color image retrieval algorithm was presented in order to retrieve image from massive images. This method firstly transformed image from RGB space to HSV space. V component of HSV space was decomposed into three levels by dual tree complex wavelet transform (DT-CWT) to extract high frequency components as texture features. Invariant moments of V component were extracted as shape features. Information of H component of HSV space was extracted as color features. Then three types of features were combined to form feature vector to judge similarity of images. Experiments show that this algorithm has not only high precision and recall but also low time consumption.

scholarly journals AN OPTIMIZED FEATURE EXTRACTION TECHNIQUE FOR CONTENT BASED IMAGE RETRIEVAL

2013 ◽  
pp. 11-16
Author(s):  
SAVITHA SIVAN ◽  
THUSNAVIS BELLA MARY. I
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Feature Fusion ◽  
Texture Features ◽  
Research Area ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Multimedia Technologies ◽  
Active Research ◽  
Occurrence Matrix

Content-based image retrieval (CBIR) is an active research area with the development of multimedia technologies and has become a source of exact and fast retrieval. The aim of CBIR is to search and retrieve images from a large database and find out the best match for the given query. Accuracy and efficiency for high dimensional datasets with enormous number of samples is a challenging arena. In this paper, Content Based Image Retrieval using various features such as color, shape, texture is made and a comparison is made among them. The performance of the retrieval system is evaluated depending upon the features extracted from an image. The performance was evaluated using precision and recall rates. Haralick texture features were analyzed at 0 o, 45 o, 90 o, 180 o using gray level co-occurrence matrix. Color feature extraction was done using color moments. Structured features and multiple feature fusion are two main technologies to ensure the retrieval accuracy in the system. GIST is considered as one of the main structured features. It was experimentally observed that combination of these techniques yielded superior performance than individual features. The results for the most efficient combination of techniques have also been presented and optimized for each class of query.

Multi-Featured Content-Based Image Retrieval Using Color and Texture Features

2016 ◽  
Vol 850 ◽  
pp. 136-143 ◽  
Author(s):  
Mehmet Ayan ◽  
O. Ayhan Erdem ◽  
Hasan Şakir Bilge
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Color Histogram ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Occurrence Matrix ◽  
Color Moment ◽  
Color And Texture Features

Content-based image retrieval (CBIR) system becomes a hot topic in recent years. CBIR system is the retrieval of images based on visual features. CBIR system based on a single feature has a low performance. Therefore, in this paper a new content based image retrieval method using color and texture features is proposed to improve performance. In this method color histogram and color moment are used for color feature extraction and grey level co-occurrence matrix (GLCM) is used for texture feature extraction. Then all extracted features are integrated for image retrieval. Finally, color histogram, color moment, GLCM and proposed methods are tested respectively. As a result, it is observed that proposed method which integrates color and texture features gave better results than the other methods used independently. To demonstrate the proposed system is successful, it was compared with existing CBIR systems. The proposed method showed superior performance than other comparative systems.

scholarly journals Texton Based Image Retrieval Using Indexed LBP Transitions

2014 ◽  
Vol 13 (12) ◽  
pp. 5286-5300 ◽  
Author(s):  
A. Srinivasa Rao ◽  
V.Venkata Krishna ◽  
Prof.YKSundara Krishna
Keyword(s):  
Image Retrieval ◽  
Data Base ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Experimental Result ◽  
Texture Image ◽  
Huge Data ◽  
Proposed Model ◽  
Occurrence Matrix ◽  
Rotationally Invariant

The present paper derived a new model of texture image retrieval by integrating the transitions on Local Binary Pattern (LBP) with textons and Grey Level Co-occurrence Matrix (GLCM). The present paper initially derived transitions that occur from 0 to 1 or 1 to 0 in circular manner on LBP. The transitions reduce the 256 LBP codes into five texture features. This reduces the lot of complexity. The LBP codes are rotationally variant. The proposed circular transitions on LBP are rotationally invariant. Textons,which represents the local relationships,are detected on this. The GLCM features are evaluated on the texton based image for efficient image retrieval. The proposed method is experimented on a huge data base of textures collected from Google data base. The experimental result indicates the efficiency of the proposed model.

scholarly journals Image Retrieval Model Analysis of Digital Library Based on Texture Characteristics

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Zhao
Keyword(s):  
Image Retrieval ◽  
Digital Library ◽  
Texture Features ◽  
Document Image ◽  
Retrieval Algorithm ◽  
Retrieval Model ◽  
Edge Information ◽  
Texture Characteristics ◽  
Retrieval Efficiency ◽  
Efficient Retrieval

A new document image retrieval algorithm is proposed in view of the inefficient retrieval of information resources in a digital library. First of all, in order to accurately characterize the texture and enhance the ability of image differentiation, this paper proposes the statistical feature method of the double-tree complex wavelet. Secondly, according to the statistical characteristic method, combined with the visual characteristics of the human eye, the edge information in the document image is extracted. On this basis, we construct the meaningful texture features and use texture features to define the characteristic descriptors of document images. Taking the descriptor as the clue, the content characteristics of the document image are combined organically, and appropriate similarity measurement criteria are used for efficient retrieval. Experimental results show that the algorithm not only has high retrieval efficiency but also reduces the complexity of the traditional document image retrieval algorithm.

Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval

2011 ◽  
pp. 44-67 ◽  
Author(s):  
Gang Zhang ◽  
Z. M. Ma ◽  
Li Yan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Medical Image ◽  
Texture Feature ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Texture Feature Extraction ◽  
Medical Image Retrieval ◽  
Image Retrieval System ◽  
Occurrence Matrix

Texture feature extraction and description is one of the important research contents in content-based medical image retrieval. The chapter first proposes a framework of content-based medical image retrieval system. It then analyzes the important texture feature extraction and description methods further, such as the co-occurrence matrix, perceptual texture features, Gabor wavelet, and so forth. Moreover, the chapter analyzes the improved methods for these methods and demonstrates their application in content-based medical image retrieval.

Average Analysis Method in Selecting Haralick’s Texture Features on Color Co-occurrence Matrix for Texture Based Image Retrieval

2016 ◽  
Vol 11 (2) ◽  
pp. 79-88
Author(s):  
Abd Rasid Mamat ◽  
Mohd Khalid Awang ◽  
Norkhairani Abdul Rawi ◽  
Mohd Isa Awang ◽  
Mohd Fadzil Abdul Kadir
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Analysis Method ◽  
Average Analysis ◽  
Occurrence Matrix

scholarly journals Bio-medical Image Retrieval using Various Statistical Methods

2019 ◽  
Vol 9 (2) ◽  
pp. 2669-2676
Keyword(s):  
Image Retrieval ◽  
Statistical Methods ◽  
Complex Disease ◽  
Textural Features ◽  
Query Image ◽  
Imaging Technologies ◽  
Disease Patterns ◽  
Combined Features ◽  
Detection And Diagnosis ◽  
Occurrence Matrix

In recent, the healthcare sectors rely more on imaging technologies for early detection and diagnosis of the disease. But, the abundant images obtained from these imaging technologies have complex disease patterns associated with them and thus an expert requires more time to analyze and arrive at the decision. Hence, the image retrieval techniques have a significant role to assist the experts by retrieving the most similar images existing in the database and also help them to compare a new scan of the patient with the top matched images and arrive at the quick decision during the diagnosis of a patient. So, we have performed our studies on the two-dimensional structural Magnetic Resonance Imaging of the Open Access Series of Imaging Studies dataset. The collected images are preprocessed and categorized into different groups based on the ventricular region of the brain. After the categorization, we employ second and higher-order statistical approaches to extract the textural features. Then the computed textural features of the images existing in the dataset are compared with the textural features of a query image to retrieve the top matched images using similarity distance as the metric. Then the image retrieval performances of the proposed hybrid based statistical methods are measured. The obtained results shows that the combined features of Gray Level Co-occurrence Matrix and Law’s Texture Energy Measure attains the highest precision across the categorized groups of a dataset and it achieves 80% precision for Group1, Group2 images and 60% precision for Group3 images.

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