scholarly journals Using Color and Texture Feature Extraction Technique to Retrieve Image

2019 ◽  
Vol 8 (11S) ◽  
pp. 915-917
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Color Images ◽  
Color Histogram ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Color Feature ◽  
Occurrence Matrix ◽  
Feature Extraction Technique

This paper proposes a content image retrieval using the texture and the color feature of the images. Although for extraction of texture feature, the “gray level co-occurrence matrix (GLCM) algorithm” is used and for extracting color feature the color histogram is used. The presented system is tested on the WANG database that contains a thousand color images with ten different classes by the help of three various type of distances

scholarly journals Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

2011 ◽  
Vol 10 (3) ◽  
pp. 73-79 ◽  
Author(s):  
Jian Yang ◽  
Jingfeng Guo
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Feature ◽  
Local Area ◽  
Image Texture ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Feature Extraction Method ◽  
Level Difference ◽  
Occurrence Matrix

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

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 Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 389
Author(s):  
Chih-Ling Huang ◽  
Meng-Jia Lian ◽  
Yi-Hsuan Wu ◽  
Wei-Ming Chen ◽  
Wen-Tai Chiu
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Neoplastic Disease ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Ovarian Adenocarcinoma ◽  
Optical Images ◽  
Adenocarcinoma Cells ◽  
Occurrence Matrix ◽  
Resistant Cells

Ovarian cancer is the most malignant of all gynecological cancers. A challenge that deteriorates with ovarian adenocarcinoma in neoplastic disease patients has been associated with the chemoresistance of cancer cells. Cisplatin (CP) belongs to the first-line chemotherapeutic agents and it would be beneficial to identify chemoresistance for ovarian adenocarcinoma cells, especially CP-resistance. Gray level co-occurrence matrix (GLCM) was characterized imaging from a numeric matrix and find its texture features. Serous type (OVCAR-4 and A2780), and clear cell type (IGROV1) ovarian carcinoma cell lines with CP-resistance were used to demonstrate GLCM texture feature extraction of images. Cells were cultured with cell density of 6 × 105 in a glass-bottom dish to form a uniform coverage of the glass slide to get the optical images by microscope and DVC camera. CP-resistant cells included OVCAR-4, A2780 and IGROV and had the higher contrast and entropy, lower energy, and homogeneity. Signal to noise ratio was used to evaluate the degree for chemoresistance of cell images based on GLCM texture feature extraction. The difference between wile type and CP-resistant cells was statistically significant in every case (p < 0.001). It is a promising model to achieve a rapid method with a more reliable diagnostic performance for identification of ovarian adenocarcinoma cells with CP-resistance by feature extraction of GLCM in vitro or ex vivo.

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.

Sign in / Sign up

Export Citation Format

Share Document