Patterned fabric image retrieval using relevant feedback via geometric similarity

2021 ◽  
pp. 004051752110362
Author(s):  
Jun Xiang ◽  
Ning Zhang ◽  
Ruru Pan ◽  
Weidong Gao
Keyword(s):  
Image Retrieval ◽  
Inventory Management ◽  
Relevance Feedback ◽  
Color Space ◽  
Feature Descriptor ◽  
Outlier Rejection ◽  
Retrieval Task ◽  
Hsv Color Space ◽  
Novel Approach ◽  
Feedback Algorithm

Due to the potential value in many areas, such as e-commerce and inventory management, fabric image retrieval, which is a special case of content-based image retrieval, has recently become a research hotspot. As a major category of textile fabrics, patterned fabrics have a diverse and complex appearance, making the retrieval task more challenging. To address this situation, this paper proposes a novel approach for patterned fabric based on the non-subsampled contourlet transform (NSCT) feature descriptor and relevance feedback technique. To integrate the color information into the NSCT feature descriptor, we extract the feature of patterned fabric images in HSV color space. An outlier rejection-based parametric relevance feedback algorithm is employed to adjust the similarity matrix to improve the retrieval results. The experimental results not only show the effectiveness of the proposed approach but also demonstrate that it can significantly improve the performance of the retrieval system compared to other state-of-the-art algorithms.

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.

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 Improving the Results of Google Scholar Engine through Automatic Query Expansion Mechanism and Pseudo Re-ranking using MVRA

2018 ◽  
Vol 42 (2) ◽  
pp. 219-229
Author(s):  
Mawloud Mosbah
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Majority Voting ◽  
Google Scholar ◽  
Content Based Image Retrieval ◽  
Ranking Algorithm ◽  
Expansion Mechanism ◽  
Feedback Algorithm ◽  
Pseudo Relevance Feedback

In this paper, we address the enhancing of Google Scholar engine, in the context of text retrieval, through two mechanisms related to the interrogation protocol of that query expansion and reformulation. The both schemes are applied with re-ranking results using a pseudo relevance feedback algorithm that we have proposed previously in the context of Content based Image Retrieval (CBIR) namely Majority Voting Re-ranking Algorithm (MVRA). The experiments conducted using ten queries reveal very promising results in terms of effectiveness.

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
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