Local tri-directional patterns: A new texture feature descriptor for image retrieval

2016 ◽  
Vol 51 ◽  
pp. 62-72 ◽  
Author(s):  
Manisha Verma ◽  
Balasubramanian Raman
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Feature Descriptor

scholarly journals Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 135608-135629
Author(s):  
Ayesha Khan ◽  
Ali Javed ◽  
Muhammad Tariq Mahmood ◽  
Muhammad Hamza Arif Khan ◽  
Ik Hyun Lee
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Feature Descriptor

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

Multi-trend binary code descriptor: a novel local texture feature descriptor for image retrieval

2017 ◽  
Vol 12 (2) ◽  
pp. 247-254 ◽  
Author(s):  
Laihang Yu ◽  
Lin Feng ◽  
Huibing Wang ◽  
Li Li ◽  
Yang Liu ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Binary Code ◽  
Texture Feature ◽  
Feature Descriptor ◽  
Local Texture

scholarly journals PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 202
Author(s):  
Muhammad Qasim ◽  
Danish Mahmood ◽  
Asifa Bibi ◽  
Mehedi Masud ◽  
Ghufran Ahmed ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Computational Cost ◽  
Texture Feature ◽  
Principal Component ◽  
Directional Pattern ◽  
Manhattan Distance ◽  
Feature Descriptor ◽  
Joint Histogram ◽  
Wide Range ◽  
Local Intensity

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.

Recognition of natural ice types on in-service glass insulators based on texture feature descriptor

2017 ◽  
Vol 24 (1) ◽  
pp. 535-542 ◽  
Author(s):  
Lin Yang ◽  
Xiaolan Jiang ◽  
Yanpeng Hao ◽  
Licheng Li ◽  
Hao Li ◽  
...  
Keyword(s):  
Texture Feature ◽  
Feature Descriptor

scholarly journals Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

2020 ◽  
Vol 9 (3) ◽  
pp. 1466-1469
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Attribute Extraction ◽  
The Individual ◽  
Selection Of ◽  
Color Texture

Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques

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