Image Retrieval Technology Based on Color Similarity Matrix

2014 ◽  
Vol 912-914 ◽  
pp. 1272-1275
Author(s):  
Shui Li Zhang ◽  
Jun Tang Dong ◽  
Zhi Yong Feng
Keyword(s):  
Image Retrieval ◽  
Euclidean Distance ◽  
Color Image ◽  
Color Space ◽  
Experimental Result ◽  
Similarity Matrix ◽  
Similarity Matching ◽  
Hsv Color Space ◽  
Quantization Effect ◽  
Color Similarity

Somecolor quantization problems concerning quantization of a color image into a fewnumbers of colors are discussed. In the HSV color space that is the best visualidentity of people, H is uniform quantified, S and V is respectivelynon-uniform quantified.Aimed at the limitation of Euclidean distance failing to consider thesimilarity between colors in the process of retrieval, a color similaritymatrix is introduced. Experimental result shows this algorithm is of certainpracticability and the quantization effect is also comparatively ideal whenmaking similarity matching

scholarly journals AN EFFICIENT APPROACH FOR CONTENT BASED RETRIEVAL USING MULTI WAVELET AND HSV COLOR SPACE

2013 ◽  
pp. 23-25
Author(s):  
E. VENKATESWARLU ◽  
K.SOUNDARA RAJAN
Keyword(s):  
Image Retrieval ◽  
Similarity Measure ◽  
Euclidean Distance ◽  
Color Space ◽  
Visual Feature ◽  
Computation Complexity ◽  
Hsv Color Space ◽  
Efficient Approach ◽  
Retrieval Accuracy ◽  
Canberra Distance

This paper presents an approach for image retrieval by using multiwavelet and hsv color space. The HSV stands for the Hue, Saturation and Value, provides the perception representation according with human visual feature. The multiwavelets offer simultaneous orthogonality, symmetry and short support. In this paper, we have tested 140 images with 5 different categories. the experimental results show the better results interms of retrieval accuracy and computation complexity. The performance of this approach is measured and results are shown. Euclidean Distance and Canberra Distance are used as similarity measure in the proposed CBIR system.

ROBUST IMAGE RETRIEVAL APPROACH BASED ON CLUSTERING ANALYSIS OF RE-QUANTIZED COLOR IMAGE

2007 ◽  
Vol 05 (01) ◽  
pp. 213-225 ◽  
Author(s):  
YA-LI JI ◽  
XIAO-PING CHENG ◽  
NAI-QIN FENG
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Feature Representation ◽  
Hsv Color Space ◽  
Invariant Moments ◽  
Robust Image ◽  
Color Clustering ◽  
Object Features ◽  
Large Image Databases

In this paper, we propose a robust approach about color image retrieval. It can realize fast matching in CBIR (Content-Based Image Retrieval) when we search in large image databases. Indexes root in object features of Z image which is the result of re-quantization in HSV color space, matching with a non-geometrical distance is based on objects, so time consumption pixel by pixel can be avoided. Because Z image is made up of many color clustering regions and invariant moments are used for feature representation, our approach is robust to translation, scale and rotation.

Content-Based Image Retrieval Using Color Volume Histograms

2019 ◽  
Vol 33 (11) ◽  
pp. 1940010 ◽  
Author(s):  
Ji-Zhao Hua ◽  
Guang-Hai Liu ◽  
Shu-Xiang Song
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Content Based Image Retrieval ◽  
Feature Descriptor ◽  
Human Visual Perception ◽  
Hsv Color Space ◽  
Spatial Features ◽  
Close Relationship ◽  
New Feature

Human visual perception has a close relationship with the HSV color space, which can be represented as a cylinder. The question of how visual features are extracted using such an attribute is important. In this paper, a new feature descriptor; namely, a color volume histogram, is proposed for image representation and content-based image retrieval. It converts a color image from RGB color space to HSV color space and then uniformly quantizes it into 72 bins of color cues and 32 bins of edge cues. Finally, color volumes are used to represent the image content. The proposed algorithm is extensively tested on two Corel datasets containing 15[Formula: see text]000 natural images. These image retrieval experiments show that the color volume histogram has the power to describe color, texture, shape and spatial features and performs significantly better than the local binary pattern histogram and multi-texton histogram approaches.

scholarly journals Image Retrieval Based on a Multi-Integration Features Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Chu ◽  
Guang-Hai Liu
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Integration Theory ◽  
Feature Descriptor ◽  
Hsv Color Space ◽  
Color Differences ◽  
Feature Integration Theory ◽  
Perception Theory ◽  
Edge Features

Feature integration theory can be regarded as a perception theory, but the extraction of visual features using such a theory within the CBIR framework is a challenging problem. To address this problem, we extract the color and edge features based on a multi-integration features model and use these for image retrieval. A novel and highly simple but efficient visual feature descriptor, namely, a multi-integration features histogram, is proposed for image representation and content-based image retrieval. First, a color image is converted from the RGB to the HSV color space, and the color features and color differences are extracted. Then, the color differences are calculated to extract the edge features using a set of simple integration processes. Finally, combining the color, edge, and spatial layout features allows representing the image content. Experiments show that our method produces results comparable to existing and well-known methods on three datasets that contain 25,000 natural images. The performances are significantly better than that of the BOW histogram, local binary pattern histogram, histogram of oriented gradient, and multi-texton histogram, with performances similar to the color volume histogram.

The Study of Forest Fire Color Image Segmentation

2011 ◽  
Vol 474-476 ◽  
pp. 2140-2145
Author(s):  
Si Li ◽  
Hong E Ren
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Color Image ◽  
Color Space ◽  
Color Images ◽  
Color Image Segmentation ◽  
Hsv Color Space ◽  
Segmentation Methods ◽  
Time Periods ◽  
Night And Day

Combined with the composition characteristics of forest fire image background when the forest fire occurred during different time periods of night and day, different image segmentation methods were applied to the forest fire color images of different time periods respectively, which could improve the efficiency of image processing. Meanwhile, application of H and S components from HSV color space, the strategy on color image segmentation which processed the segmentation processing to forest fire color images with complicated background was proposed combined with Otsu algorithm. The results of simulation experiment showed that the above-mentioned segmentation methods were obtained satisfactory segmentation effects when the segmentation on forest fire color images during different time periods of night and day were processed respectively. And also application of Otsu algorithm based on HSV color model, the forest fire image segmentation occurred in the daytime was processed, which overcame the interference factors of light and smoke, as well as the shortage of noise sensibility due to Otsu algorithm.

Object Tracking System in Dynamic Scene Based on Improved Camshift Algorithm and Kalman Filter

2014 ◽  
Vol 602-605 ◽  
pp. 2061-2064 ◽  
Author(s):  
Chao Bing Liu ◽  
Cong Cong Chen ◽  
Xiao Li
Keyword(s):  
Kalman Filter ◽  
Tracking System ◽  
Mean Shift ◽  
Color Space ◽  
Image Sequence ◽  
Experimental Result ◽  
Dynamic Scene ◽  
Hsv Color Space ◽  
Mean Shift Algorithm ◽  
Camshift Algorithm

Camshift, namely "Continuously Adaptive Mean-Shift" algorithm, is an adaptive tracking algorithm. This algorithm is based on the color information to track the moving target in image sequence. In the simple background, this algorithm achieved a steady and current tracking effect. But in dynamic scene, the global motion caused by the camera, the background of the light and occlusion will affect the accuracy, or even lose the tracking of the target. In order to solve the above problem, this paper adjust the H component in HSV color space, as well use weighted color histogram to improve the Camshift algorithm, then combined with Kalman filter to track the target in the image sequence. The experimental result shows that this approach can track object stability and correctly in dynamic scene.

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