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.

scholarly journals Global Distribution Adjustment and Nonlinear Feature Transformation for Automatic Colorization

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Terumasa Aoki ◽  
Van Nguyen
Keyword(s):  
Image Matching ◽  
Feature Vector ◽  
Color Image ◽  
Computational Cost ◽  
Texture Feature ◽  
Feature Descriptor ◽  
Matching Methods ◽  
Nonlinear Feature ◽  
Efficient Matching ◽  
Descriptive Feature

Automatic colorization is generally classified into two groups: propagation-based methods and reference-based methods. In reference-based automatic colorization methods, color image(s) are used as reference(s) to reconstruct original color of a gray target image. The most important task here is to find the best matching pairs for all pixels between reference and target images in order to transfer color information from reference to target pixels. A lot of attractive local feature-based image matching methods have already been developed for the last two decades. Unfortunately, as far as we know, there are no optimal matching methods for automatic colorization because the requirements for pixel matching in automatic colorization are wholly different from those for traditional image matching. To design an efficient matching algorithm for automatic colorization, clustering pixel with low computational cost and generating descriptive feature vector are the most important challenges to be solved. In this paper, we present a novel method to address these two problems. In particular, our work concentrates on solving the second problem (designing a descriptive feature vector); namely, we will discuss how to learn a descriptive texture feature using scaled sparse texture feature combining with a nonlinear transformation to construct an optimal feature descriptor. Our experimental results show our proposed method outperforms the state-of-the-art methods in terms of robustness for color reconstruction for automatic colorization applications.

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

Based on Texture Feature of Color Image Retrieval

2012 ◽  
Vol 268-270 ◽  
pp. 1748-1751
Author(s):  
Jin Hui Lin ◽  
Ji Xiang Zhang
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Effective Means ◽  
Texture Feature ◽  
Research Field ◽  
Image Texture ◽  
Content Based Image Retrieval ◽  
Wide Range ◽  
Texture Characteristics ◽  
Characteristics Of Image

Along with the development of multimedia technology, large capacity image library a wide range of applications, content-based image retrieval (CBIR) technology is management and retrieval effective means. Among the contend-based retrieval techno-logiest, feature extraction is most important. For instance, color, texture and shape feature etc. But each feature of image can only catch one aspect of the similarity of image, how to represent images better has become a important research field in content -based image retrieval. Introduced the use of image texture characteristics of image retrieval method, with gray symbiotic matrix algorithm, from the block of the texture characteristics, and gives the CBIR system is realized, and the experiment results are given.

scholarly journals Novel Texture Feature Descriptors Based on Multi-Fractal Analysis and LBP for Classifying Breast Density in Mammograms

2021 ◽  
Vol 7 (10) ◽  
pp. 205
Author(s):  
Haipeng Li ◽  
Ramakrishnan Mukundan ◽  
Shelley Boyd
Keyword(s):  
Breast Density ◽  
Fractal Analysis ◽  
Classification Accuracy ◽  
Texture Feature ◽  
Principal Component ◽  
Local Binary Patterns ◽  
Classification Model ◽  
Feature Descriptor ◽  
Full Field ◽  
Mammogram Images

This paper investigates the usefulness of multi-fractal analysis and local binary patterns (LBP) as texture descriptors for classifying mammogram images into different breast density categories. Multi-fractal analysis is also used in the pre-processing step to segment the region of interest (ROI). We use four multi-fractal measures and the LBP method to extract texture features, and to compare their classification performance in experiments. In addition, a feature descriptor combining multi-fractal features and multi-resolution LBP (MLBP) features is proposed and evaluated in this study to improve classification accuracy. An autoencoder network and principal component analysis (PCA) are used for reducing feature redundancy in the classification model. A full field digital mammogram (FFDM) dataset, INBreast, which contains 409 mammogram images, is used in our experiment. BI-RADS density labels given by radiologists are used as the ground truth to evaluate the classification results using the proposed methods. Experimental results show that the proposed feature descriptor based on multi-fractal features and LBP result in higher classification accuracy than using individual texture feature sets.

scholarly journals Canopy Height Estimation from Single Multispectral 2D Airborne Imagery Using Texture Analysis and Machine Learning in Structurally Rich Temperate Forests

2019 ◽  
Vol 11 (23) ◽  
pp. 2853
Author(s):  
Christos Boutsoukis ◽  
Ioannis Manakos ◽  
Marco Heurich ◽  
Anastasios Delopoulos
Keyword(s):  
Land Cover ◽  
Texture Analysis ◽  
Spatial Scales ◽  
Computational Cost ◽  
Texture Feature ◽  
Cost Effective ◽  
Canopy Height ◽  
Support Vector ◽  
Structural Parameter ◽  
Wide Range

Canopy height is a fundamental biophysical and structural parameter, crucial for biodiversity monitoring, forest inventory and management, and a number of ecological and environmental studies and applications. It is a determinant for linking the classification of land cover to habitat categories towards building one-to-one relationships. Light detection and ranging (LiDAR) or 3D Stereoscopy are the commonly used and most accurate remote sensing approaches to measure canopy height. However, both require significant time and budget resources. This study proposes a cost-effective methodology for canopy height approximation using texture analysis on a single 2D image. An object-oriented approach is followed using land cover (LC) map as segmentation vector layer to delineate landscape objects. Global texture feature descriptors are calculated for each land cover object and used as variables in a number of classifiers, including single and ensemble trees, and support vector machines. The aim of the analysis is the discrimination among classes in a wide range of height values used for habitat mapping (from less than 5 cm to 40 m). For that task, different spatial resolutions are tested, representing a range from airborne to spaceborne quality ones, as well as their combinations, forming a multiresolution training set. Multiple dataset alternatives are formed based on the missing data handling, outlier removal, and data normalization techniques. The approach was applied using orthomosaics from DMC II airborne images, and evaluated against a reference LiDAR-derived canopy height model (CHM). Results reached overall object-based accuracies of 67% with the percentage of total area correctly classified exceeding 88%. Sentinel-2 simulation and multiresolution analysis (MRA) experiments achieved even higher accuracies of up to 85% and 91%, respectively, at reduced computational cost, showing potential in terms of transferability of the framework to large spatial scales.

Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model

2013 ◽  
pp. 239-261
Author(s):  
R. Krishnamoorthi ◽  
S. Sathiya Devi
Keyword(s):  
Image Retrieval ◽  
Orthogonal Polynomials ◽  
Distance Measure ◽  
Feature Vector ◽  
Gabor Filter ◽  
Computational Cost ◽  
Texture Feature ◽  
Image Data ◽  
Scale Invariant ◽  
Retrieval Technique

The exponential growth of digital image data has created a great demand for effective and efficient scheme and tools for browsing, indexing and retrieving images from a collection of large image databases. To address such a demand, this paper proposes a new content based image retrieval technique with orthogonal polynomials model. The proposed model extracts texture features that represent the dominant directions, gray level variations and frequency spectrum of the image under analysis and the resultant texture feature vector becomes rotation and scale invariant. A new distance measure in the frequency domain called Deansat is proposed as a similarity measure that uses the proposed feature vector for efficient image retrieval. The efficiency of the proposed retrieval technique is experimented with the standard Brodatz, USC-SIPI and VisTex databases and is compared with Discrete Cosine Transform (DCT), Tree Structured Wavelet Transform (TWT) and Gabor filter based retrieval schemes. The experimental results reveal that the proposed method outperforms well with less computational cost.

Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model

2011 ◽  
Vol 1 (4) ◽  
pp. 27-49
Author(s):  
R. Krishnamoorthi ◽  
S. Sathiya Devi
Keyword(s):  
Image Retrieval ◽  
Orthogonal Polynomials ◽  
Distance Measure ◽  
Feature Vector ◽  
Gabor Filter ◽  
Computational Cost ◽  
Texture Feature ◽  
Image Data ◽  
Scale Invariant ◽  
Retrieval Technique

The exponential growth of digital image data has created a great demand for effective and efficient scheme and tools for browsing, indexing and retrieving images from a collection of large image databases. To address such a demand, this paper proposes a new content based image retrieval technique with orthogonal polynomials model. The proposed model extracts texture features that represent the dominant directions, gray level variations and frequency spectrum of the image under analysis and the resultant texture feature vector becomes rotation and scale invariant. A new distance measure in the frequency domain called Deansat is proposed as a similarity measure that uses the proposed feature vector for efficient image retrieval. The efficiency of the proposed retrieval technique is experimented with the standard Brodatz, USC-SIPI and VisTex databases and is compared with Discrete Cosine Transform (DCT), Tree Structured Wavelet Transform (TWT) and Gabor filter based retrieval schemes. The experimental results reveal that the proposed method outperforms well with less computational cost.

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