Improvement on Gabor Texture Feature Based Biometric Analysis Using Image Blurring

In the last years, several works on automatic image-based food recognition have been proposed, often based on texture feature extraction and classification. However, there is still a lack of proper comparisons to evaluate which approaches are better suited for this specific task. In this work, we adopt a Random Forest classifier to measure the performances of different texture filter banks and feature encoding techniques on three different food image datasets. Comparative results are given to show the performance of each considered approach, as well as to compare the proposed Random Forest classifiers with other feature-based state-of-the-art solutions.

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Color and Texture Feature Based Scene Classification

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9254 ◽

2020 ◽

Vol 17 (11) ◽

pp. 4897-4901

Author(s):

V. Gayathri ◽

Eric Clapten ◽

S. Mahalakshmi ◽

S. Rajes Kannan

Keyword(s):

Land Use ◽

Feature Extraction ◽

Texture Feature ◽

Rotation Invariance ◽

Scene Classification ◽

Work Of Art ◽

Image Brightness ◽

Spatial Features ◽

Neighborhood Function ◽

Feature Based

Right now, overall trademark based multiscale multiresolution multistructure (M3LBP) neighborhood parallel example and nearby characteristic based totally min blend feature extraction is proposed for scene category. To extract international functions, characterize the leading spatial features in a couple of scale, a couple of choice, more than one structure way. The micro/macro shape facts and rotation invariance are guaranteed inside the worldwide function extraction approach. Neighborhood function extraction, coloration histogram characteristic (CHF) can thoroughly explain the spatial coloration statistics of an image. It also describes the image brightness, color statistics of a photo, which encompass the picture coloration distribution, photo assessment. The CHF can be computed from the min max shade quantizes. Ultimately Fused feature instance amongst nearby and international capabilities because the scene descriptor to prepare a portion based absolutely extreme finding a workable pace for scene style is outfitted. The proposed strategy is radically assessed on benchmark scene datasets (the 21 magnificence land use scene), and the trial results show that the proposed procedure prompts predominant kind standard execution as contrasted and the realm of-work of art style systems.

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Texture feature based interaction maps: potential and limits

Advances in Computer Vision ◽

10.1007/978-3-7091-6867-7_9 ◽

1997 ◽

pp. 79-88 ◽

Cited By ~ 1

Author(s):

Dmitry Chetverikov

Keyword(s):

Texture Feature ◽

Feature Based

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Gabor-oriented local order feature-based deep learning for face annotation

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691319500322 ◽

2019 ◽

Vol 17 (05) ◽

pp. 1950032

Author(s):

A. Kasthuri ◽

A. Suruliandi ◽

S. P. Raja

Keyword(s):

Semantic Network ◽

Real Life ◽

Texture Feature ◽

Texture Features ◽

Feature Representation ◽

Local Order ◽

Face Images ◽

Novel Approach ◽

The Face ◽

Feature Based

Face annotation, a modern research topic in the area of image processing, has useful real-life applications. It is a really difficult task to annotate the correct names of people to the corresponding faces because of the variations in facial appearance. Hence, there still is a need for a robust feature to improve the performance of the face annotation process. In this work, a novel approach called the Deep Gabor-Oriented Local Order Features (DGOLOF) for feature representation has been proposed, which extracts deep texture features from face images. Seven recently proposed face annotation methods are considered to evaluate the proposed deep texture feature under uncontrolled situations like occlusion, expression changes, illumination and pose variations. Experimental results on the LFW, IMFDB, Yahoo and PubFig databases show that the proposed deep texture feature provides efficient results with the Name Semantic Network (NSN)-based face annotation. Moreover, it is observed that the proposed deep texture feature improves the performance of face annotation, regardless of all the challenges involved.

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High Dimensional Feature for Hyperspectral Image Classification

MATEC Web of Conferences ◽

10.1051/matecconf/201824603041 ◽

2018 ◽

Vol 246 ◽

pp. 03041

Author(s):

Cailing Wang ◽

Hongwei Wang ◽

Yinyong Zhang ◽

Jia Wen ◽

Fan Yang

Keyword(s):

Image Classification ◽

Hyperspectral Image ◽

Texture Feature ◽

High Dimensional ◽

Support Vector ◽

Hyperspectral Image Classification ◽

Feature Based ◽

Pca Method ◽

The One ◽

Low Dimensional

Making a high dimensional (e.g., 100k-dim) feature for hyperspectral image classification seems not a good idea because it will bring difficulties on consequent training, computation, and storage. In this paper, we study the performance of a high-dimensional feature by texture feature. The texture feature based on multi-local binary pattern descriptor, can achieve significant improvements over both its tradition version and the one we proposed in our previous work. We also make the high-dimensional feature practical, we employ the PCA method for dimension reduction and support vector machine for hyperspectral image classification. The two real hyperspectral image datasets are employed. Our experimental results with real hyperspectral images indicate that the high dimensional feature can enhance the classification accuracy than some low dimensional.

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