Local neighborhood difference pattern: A new feature descriptor for natural and texture image retrieval

2017 ◽  
Vol 77 (10) ◽  
pp. 11843-11866 ◽  
Author(s):  
Manisha Verma ◽  
Balasubramanian Raman
Keyword(s):  
Image Retrieval ◽  
Feature Descriptor ◽  
Texture Image ◽  
Local Neighborhood ◽  
New Feature ◽  
Difference Pattern

Local Directional Extrema Number Pattern: A New Feature Descriptor for Computed Tomography Image Retrieval

2021 ◽  
Author(s):  
Adhiyaman Manickam ◽  
Rajkumar Soundrapandiyan ◽  
Suresh Chandra Satapathy ◽  
R. Dinesh Jackson Samuel ◽  
Sujatha Krishnamoorthy ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Retrieval ◽  
Feature Descriptor ◽  
Number Pattern ◽  
Tomography Image ◽  
New Feature ◽  
Computed Tomography Image

Local Neighborhood Intensity Pattern–A new texture feature descriptor for image retrieval

2018 ◽  
Vol 113 ◽  
pp. 100-115 ◽  
Author(s):  
Prithaj Banerjee ◽  
Ayan Kumar Bhunia ◽  
Avirup Bhattacharyya ◽  
Partha Pratim Roy ◽  
Subrahmanyam Murala
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Feature Descriptor ◽  
Intensity Pattern ◽  
Local Neighborhood

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.

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