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 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.

Text-Image Retrieval With Salient Features

2021 ◽  
Vol 32 (4) ◽  
pp. 1-13
Author(s):  
Xia Feng ◽  
Zhiyi Hu ◽  
Caihua Liu ◽  
W. H. Ip ◽  
Huiying Chen
Keyword(s):  
Image Retrieval ◽  
Recall Rate ◽  
Image Features ◽  
Feature Representation ◽  
Image Feature ◽  
Text And Image ◽  
Retrieval Task ◽  
Retrieval Method ◽  
Salient Features ◽  
Object Level

In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.

Image Retrieval Based on Color Histogram of Saliency Map

2014 ◽  
Vol 989-994 ◽  
pp. 3552-3555 ◽  
Author(s):  
Jun Feng Wu ◽  
Xian Qiang Lv ◽  
Wen Lian Yang ◽  
Ye Tao ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Spatial Information ◽  
Color Image ◽  
Intensity Variation ◽  
Saliency Map ◽  
Color Histogram ◽  
The Internet ◽  
Color Histograms ◽  
Image Retrieval System ◽  
Robust Image

With the development of the internet, more and more images appear in the internet. How to effectively retrieve the desired image is still an important problem. In the past, traditional color histogram is used image retrieval system, but color histograms lack spatial information and are sensitive to intensity variation, color distortion and cropping. As a result, images with similar histograms may have totally different semantics. So the spatial information should be included in color histogram. The color histogram based on saliency map approach is introduced to overcome the above limitations. In this paper, we present a robust image retrieval based on color histogram of saliency map. Firstly, in order to extract useful spatial information of each pixel, the steady saliency map of the images is extracted. Then, color histogram based on saliency map is introduced, and the similarity between color images is computed by using the color histogram of saliency map. Experimental results show that the proposed color image retrieval is more accurate and efficient in retrieving the user-interested images.

scholarly journals Integration of Statistical based Texture and Color Feature for Medical Image Retrieval

2019 ◽  
Vol 8 (3) ◽  
pp. 5584-5588 ◽  
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Image Retrieval ◽  
Medical Image ◽  
Spatial Information ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Retrieval Method ◽  
Medical Image Retrieval ◽  
The Common

Today, the common problem in the domain of computer vision and pattern recognition is content based image retrieval (CBIR). In this paper, a novel image retrieval method using the geometric details based on the correlation among edgels and correlation between pixels has been introduced. The autocorrelation based choridiogram descriptor has been extracted from the image to obtain geometric, texture and spatial information. Color autocorrelogram has been computed to obtain color, texture and spatial information. The proposed method is tested on benchmark heterogeneous medical image database and LIDC-IDRI-CT and VIA/I-ELCAP-CT databases and results are compared with typical CBIR system for medical image retrieval

Sign in / Sign up

Export Citation Format

Share Document