An Efficient Image Retrieval Based on Combined Features

2013 ◽  
Vol 787 ◽  
pp. 1025-1029
Author(s):  
Ching Hung Su ◽  
Huang Sen Chiu ◽  
Mohd Helmy Abd Wahab ◽  
Tsai Ming Hsieh
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Texture Feature ◽  
Texture Features ◽  
Texture Descriptor ◽  
Color Code ◽  
Hsv Color Space ◽  
Retrieval Scheme ◽  
Combined Features ◽  
Color Pixel

An efficient image retrieval scheme to retrieve images is proposed based on the issue of texture and color space features extractions. The algorithm for an effective image retrieval scheme to retrieve images is presented. We propose a scheme using color and texture features and address the unique algorithm to extract the color pixel features by the HSV color space and the texture features of Homogeneous Texture Descriptor (HTD). The proposed scheme transfers each image to a quantized color code using the regulations of the properties in compliance with HSV color space model and then employing the quantized color code along with the texture feature of Homogeneous Texture Descriptor (HTD) to compare the images of database. Experimental of the proposed scheme performed on SIMPLIcity image database to demonstrate more efficient and effective than the conventional schemes.

Color and Texture Features Based Image Retrieval

2013 ◽  
Vol 441 ◽  
pp. 707-710
Author(s):  
Ching I Lin ◽  
Ching Hung Su ◽  
Shih Hung Tai
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Texture Features ◽  
Color Code ◽  
Hsv Color Space ◽  
Space Model ◽  
Retrieval Scheme ◽  
Color Pixel ◽  
Color And Texture Features

We propose a practical image retrieval scheme to retrieve images efficiently. We propose a scheme using color and texture features and address the unique algorithm to extract the color pixel features by the HSV color space and Tamura features of the texture features. The proposed scheme transfers each image to a quantized color code using the regulations of the properties in compliance with HSV color space model and then employing the quantized color code along with Tamura features of texture features to compare the images of database. Experimental of the proposed scheme on demonstrate more efficient and effective than the conventional schemes.

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.

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.

scholarly journals Content Based Image Retrieval using Multi-level 3D Color Texture and Low Level Color Features with Neural Network Based Classification System

2021 ◽  
Vol 15 ◽  
pp. 265-270
Author(s):  
Priyesh Tiwari ◽  
Shivendra Nath Sharan ◽  
Kulwant Singh ◽  
Suraj Kamya
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Model ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Multi Level ◽  
Different Color ◽  
Color Texture

Content based image retrieval (CBIR), is an application of real-world computer vision domain where from a query image, similar images are searched from the database. The research presented in this paper aims to find out best features and classification model for optimum results for CBIR system.Five different set of feature combinations in two different color domains (i.e., RGB & HSV) are compared and evaluated using Neural Network Classifier, where best results obtained are 88.2% in terms of classifier accuracy. Color moments feature used comprises of: Mean, Standard Deviation,Kurtosis and Skewness. Histogram features is calculated via 10 probability bins. Wang-1k dataset is used to evaluate the CBIR system performance for image retrieval.Research concludes that integrated multi-level 3D color-texture feature yields most accurate results and also performs better in comparison to individually computed color and texture features.

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
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