On the design of a color image retrieval method based on combined color descriptors and features

2016 ◽  
Author(s):  
Hung-Hsu Tsai ◽  
Bae-Muu Chang ◽  
Pei-Shan Lo ◽  
Jie-Yan Peng
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Retrieval Method ◽  
Color Descriptors

A CONTEXT-BASED APPROACH FOR COLOR IMAGE RETRIEVAL

2002 ◽  
Vol 16 (02) ◽  
pp. 239-255 ◽  
Author(s):  
JAU-LING SHIH ◽  
LING-HWEI CHEN
Keyword(s):  
Image Retrieval ◽  
Similarity Measure ◽  
Relevance Feedback ◽  
Color Image ◽  
Experimental Results ◽  
Retrieval Method ◽  
Feature Vectors ◽  
The Mean ◽  
Feedback Algorithm

In this paper, a color image retrieval method based on the primitives of images will be proposed. First, the context of each pixel in an image will be defined. Then, the contexts in the image are clustered into several classes based on the algorithm of fast noniterative clustering. The mean of the context in the same class is considered as a primitive of the image. The primitives are used as feature vectors. Since the numbers of primitives between images are different, a specially designed similarity measure is then proposed to do color image retrieval. To better adapt to the preferences of users, a relevance feedback algorithm is provided to automatically determine the weight of each primitive according to the user's response. To demonstrate the effectiveness of the proposed system, several test databases from Corel are used to compare the performances of the proposed system with other methods. The experimental results show that the proposed system is superior to others.

A COLOR IMAGE RETRIEVAL METHOD BASED ON COLOR MOMENT AND COLOR VARIANCE OF ADJACENT PIXELS

2002 ◽  
Vol 16 (01) ◽  
pp. 113-125 ◽  
Author(s):  
YUNG-KUAN CHAN ◽  
CHIN-CHEN CHANG
Keyword(s):  
Image Retrieval ◽  
Similarity Measure ◽  
Spatial Information ◽  
Color Image ◽  
Image Features ◽  
Experimental Results ◽  
Retrieval Method ◽  
Color Moment

This paper first introduces three simple and effective image features — the color moment (CM), the color variance of adjacent pixels (CVAP) and CM–CVAP. The CM feature delineates the color-spatial information of images, and the CVAP feature describes the color variance of pixels in an image. However, these two features can only characterize the content of images in different ways. This paper hence provides another feature CM–CVAP, which combines both, to raise the quality of similarity measure. The experimental results show that the image retrieval method based on the CM–CVAP feature gives quite an impressive performance.

A NOVEL TWO-LEVEL COLOR IMAGE RETRIEVAL METHOD

2011 ◽  
Vol 11 (03) ◽  
pp. 339-353 ◽  
Author(s):  
XING-YUAN WANG ◽  
ZHI-FENG CHEN ◽  
JIAO-JIAO YUN
Keyword(s):  
Image Retrieval ◽  
Digital Image ◽  
Color Image ◽  
Image Databases ◽  
Color Model ◽  
Retrieval Method ◽  
Retrieval Accuracy ◽  
Similar Images

This paper presents an effective two-level color image retrieval method based on the RGB color model. For the purpose of effectively retrieving more similar images from the digital image databases, we divide the image into different regions and set bigger weight for the region we focus on. In addition, we set different weights for each RGB component of the color image according to the main hue of it. As a result, this scheme can enhance the retrieval accuracy that is measured in terms of the recall and precision.

Sign in / Sign up

Export Citation Format

Share Document