Multiscale Analysis for Improving Texture Classification
- URL: http://arxiv.org/abs/2204.09841v1
- Date: Thu, 21 Apr 2022 01:32:22 GMT
- Title: Multiscale Analysis for Improving Texture Classification
- Authors: Steve T. M. Ataky and Diego Saqui and Jonathan de Matos and Alceu S.
Britto Jr. and Alessandro L. Koerich
- Abstract summary: This paper employs the Gaussian-Laplacian pyramid to treat different spatial frequency bands of a texture separately.
We aggregate features extracted from gray and color texture images using bio-inspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix features, and Haralick statistical features into a single feature vector.
- Score: 62.226224120400026
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Information from an image occurs over multiple and distinct spatial scales.
Image pyramid multiresolution representations are a useful data structure for
image analysis and manipulation over a spectrum of spatial scales. This paper
employs the Gaussian-Laplacian pyramid to treat different spatial frequency
bands of a texture separately. First, we generate three images corresponding to
three levels of the Gaussian-Laplacian pyramid for an input image to capture
intrinsic details. Then we aggregate features extracted from gray and color
texture images using bio-inspired texture descriptors, information-theoretic
measures, gray-level co-occurrence matrix features, and Haralick statistical
features into a single feature vector. Such an aggregation aims at producing
features that characterize textures to their maximum extent, unlike employing
each descriptor separately, which may lose some relevant textural information
and reduce the classification performance. The experimental results on texture
and histopathologic image datasets have shown the advantages of the proposed
method compared to state-of-the-art approaches. Such findings emphasize the
importance of multiscale image analysis and corroborate that the descriptors
mentioned above are complementary.
Related papers
- A Multi-scale Information Integration Framework for Infrared and Visible
Image Fusion [50.84746752058516]
Infrared and visible image fusion aims at generating a fused image containing intensity and detail information of source images.
Existing methods mostly adopt a simple weight in the loss function to decide the information retention of each modality.
We propose a multi-scale dual attention (MDA) framework for infrared and visible image fusion.
arXiv Detail & Related papers (2023-12-07T14:40:05Z) - Pyramid Texture Filtering [86.15126028139736]
We present a simple but effective technique to smooth out textures while preserving the prominent structures.
Our method is built upon a key observation -- the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures.
We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts.
arXiv Detail & Related papers (2023-05-11T02:05:30Z) - Texture image analysis based on joint of multi directions GLCM and local
ternary patterns [0.0]
Texture features can be used in many different applications in commuter vision or machine learning problems.
New approach is proposed based on combination of two texture descriptor, co-occurrence matrix and local ternary patterns.
Experimental results show that proposed approach provide higher classification rate in comparison with some state-of-the-art approaches.
arXiv Detail & Related papers (2022-09-05T09:53:00Z) - Texture features in medical image analysis: a survey [0.0]
Texture, color and shape are three main components which are used by human visual system to recognize image contents.
Some state-of-the-art methods are survived that use texture analysis in medical applications and disease diagnosis.
Results demonstrate that texture features separately or in joint of different feature sets such as deep, color or shape features provide high accuracy in medical image classification.
arXiv Detail & Related papers (2022-08-02T15:31:10Z) - A 3-stage Spectral-spatial Method for Hyperspectral Image Classification [2.28438857884398]
We propose a novel framework that utilizes both spatial and spectral information for classifying pixels in hyperspectral images.
We demonstrate the superiority of our method against three state-of-the-art algorithms on six benchmark hyperspectral data sets.
arXiv Detail & Related papers (2022-04-20T08:23:05Z) - Incorporating Texture Information into Dimensionality Reduction for
High-Dimensional Images [65.74185962364211]
We present a method for incorporating neighborhood information into distance-based dimensionality reduction methods.
Based on a classification of different methods for comparing image patches, we explore a number of different approaches.
arXiv Detail & Related papers (2022-02-18T13:17:43Z) - Fractal measures of image local features: an application to texture
recognition [1.2183405753834562]
We compute the box counting dimension of the local binary codes thresholded at different levels to compose the feature vector.
The proposed method demonstrated to be competitive with other state-of-the-art solutions reported in the literature.
arXiv Detail & Related papers (2021-08-27T20:27:28Z) - Dynamic Texture Recognition via Nuclear Distances on Kernelized
Scattering Histogram Spaces [95.21606283608683]
This work proposes to describe dynamic textures as kernelized spaces of frame-wise feature vectors computed using the Scattering transform.
By combining these spaces with a basis-invariant metric, we get a framework that produces competitive results for nearest neighbor classification and state-of-the-art results for nearest class center classification.
arXiv Detail & Related papers (2021-02-01T13:54:24Z) - Image Quality Assessment: Unifying Structure and Texture Similarity [38.05659069533254]
We develop the first full-reference image quality model with explicit tolerance to texture resampling.
Using a convolutional neural network, we construct an injective and differentiable function that transforms images to overcomplete representations.
arXiv Detail & Related papers (2020-04-16T16:11:46Z) - Pathological Retinal Region Segmentation From OCT Images Using Geometric
Relation Based Augmentation [84.7571086566595]
We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape.
The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
arXiv Detail & Related papers (2020-03-31T11:50:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.