Optimization of image retrieval by using HSV color space, Zernike moment & DWT technique

2015 ◽  
Author(s):  
Shilky Shrivastava ◽  
Bhoomika Gupta ◽  
Manish Gupta
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Zernike Moment ◽  
Hsv Color Space

scholarly journals Content-Based Image Retrieval using Color Quantization and Angle Representation

2014 ◽  
Vol 13 (10) ◽  
pp. 5094-5104
Author(s):  
Ihab Zaqout
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Measurement Methods ◽  
Content Based Image Retrieval ◽  
Similarity Measurement ◽  
Color Quantization ◽  
Human Visual Perception ◽  
Hsv Color Space ◽  
Perception System ◽  
Similar Images

An efficient non-uniform color quantization and similarity measurement methods are proposed to enhance the content-based image retrieval (CBIR) applications. The HSV color space is selected because it is close to human visual perception system, and a non-uniform color method is proposed to quantize an image into 37 colors. The marker histogram (MH) vector of size 296 values is generated by segmenting the quantized image into 8 regions (multiplication of 45°) and count the occurrences of the quantized colors in their particular angles. To cope with rotated images, an incremental displacement to the MH is applied 7 times. To find similar images, we proposed a new similarity measurement and other 4 existing metrics. A uniform color quantization of related work is implemented too and compared to our quantization method. One-hundred test images are selected from the Corel-1000 images database. Our experimental results conclude high retrieving precision ratios compared to other techniques.

Content Based Images Retrieval Based on HSV Color Space and GLCM

2014 ◽  
Vol 644-650 ◽  
pp. 4287-4290
Author(s):  
Ching Hun Su ◽  
Huang Sen Chiu ◽  
Tsai Ming Hsieh
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Color Space ◽  
Texture Feature ◽  
Hsv Color Space ◽  
Retrieval Scheme ◽  
Image Retrieval System ◽  
Retrieval Problem ◽  
Occurrence Matrix ◽  
Better Than

We propose a practical image retrieval scheme to retrieve images efficiently. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Gray Level Co-occurrence matrix to compare the images of database. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues of both the content based image retrieval system and a text based image retrieval system. Experimental results reveal that proposed scheme is better than the conventional methodologies.

Single Color Sketch-Based Image Retrieval in HSV Color Space

2020 ◽  
pp. 77-90
Author(s):  
Yu Xia ◽  
Shuangbu Wang ◽  
Yanran Li ◽  
Lihua You ◽  
Xiaosong Yang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Hsv Color Space

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.

scholarly journals Retrieval of Images using Combination of Features as Color, Color Moments and Hu Moments

2019 ◽  
Vol 7 (5) ◽  
pp. 09-21
Author(s):  
RAJKUMAR RAJ ◽  
Dr. M V Sudhamani
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Retrieval Performance ◽  
Recall Accuracy ◽  
Hsv Color Space ◽  
Low Level ◽  
Digital Era ◽  
Retrieval Systems ◽  
Hu Moments ◽  
Similar Images

In today’s digital era, several of the image retrieval systems focus on retrieving images using features from images themselves such as color, shape and textures and are referred as low-level features. In this proposed work, the features like color with HSV color space, color moments and Hu moments are employed to retrieve similar images. Various experimentations were conducted on Wang’s database images to test the combination of features for higher performance using precision, recall, accuracy and f-score. The results obtained are compared with one another and also with existing works. The retrieval performance is found to be high for proposed system against existing works.

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.

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