Texture image classification based on a pseudo-parabolic diffusion model

• URL: http://arxiv.org/abs/2011.07173v2
• Date: Sun, 24 Jan 2021 00:39:00 GMT
• Title: Texture image classification based on a pseudo-parabolic diffusion model
• Authors: Jardel Vieira, Eduardo Abreu, Joao B. Florindo
• Abstract summary: The proposed approach is tested on the classification of well established benchmark texture databases and on a practical task of plant species recognition. The good performance can be justified to a large extent by the ability of the pseudo-parabolic operator to smooth possibly noisy details inside homogeneous regions of the image.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work proposes a novel method based on a pseudo-parabolic diffusion process to be employed for texture recognition. The proposed operator is applied over a range of time scales giving rise to a family of images transformed by nonlinear filters. Therefore each of those images are encoded by a local descriptor (we use local binary patterns for that purpose) and they are summarized by a simple histogram, yielding in this way the image feature vector. The proposed approach is tested on the classification of well established benchmark texture databases and on a practical task of plant species recognition. In both cases, it is compared with several state-of-the-art methodologies employed for texture recognition. Our proposal outperforms those methods in terms of classification accuracy, confirming its competitiveness. The good performance can be justified to a large extent by the ability of the pseudo-parabolic operator to smooth possibly noisy details inside homogeneous regions of the image at the same time that it preserves discontinuities that convey critical information for the object description. Such results also confirm that model-based approaches like the proposed one can still be competitive with the omnipresent learning-based approaches, especially when the user does not have access to a powerful computational structure and a large amount of labeled data for training.

Related papers

• A Bayesian Approach to Weakly-supervised Laparoscopic Image Segmentation [1.9639956888747314]
  We study weakly-supervised laparoscopic image segmentation with sparse annotations. We introduce a novel Bayesian deep learning approach designed to enhance both the accuracy and interpretability of the model's segmentation.
  arXiv  Detail & Related papers  (2024-10-11T04:19:48Z)
• Reinforcing Pre-trained Models Using Counterfactual Images [54.26310919385808]
  This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. We identify model weaknesses by testing the model using the counterfactual image dataset. We employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model.
  arXiv  Detail & Related papers  (2024-06-19T08:07:14Z)
• Deblurring via Stochastic Refinement [85.42730934561101]
  We present an alternative framework for blind deblurring based on conditional diffusion models. Our method is competitive in terms of distortion metrics such as PSNR.
  arXiv  Detail & Related papers  (2021-12-05T04:36:09Z)
• Data Generation using Texture Co-occurrence and Spatial Self-Similarity for Debiasing [6.976822832216875]
  We propose a novel de-biasing approach that explicitly generates additional images using texture representations of oppositely labeled images. Every new generated image contains similar spatial information from a source image while transferring textures from a target image of opposite label. Our model integrates a texture co-occurrence loss that determines whether a generated image's texture is similar to that of the target, and a spatial self-similarity loss that determines whether the spatial details between the generated and source images are well preserved.
  arXiv  Detail & Related papers  (2021-10-15T08:04:59Z)
• An application of a pseudo-parabolic modeling to texture image recognition [0.0]
  We present a novel methodology for texture image recognition using a partial differential equation modeling. We employ the pseudo-parabolic Buckley-Leverett equation to provide a dynamics to the digital image representation and collect local descriptors from those images evolving in time.
  arXiv  Detail & Related papers  (2021-02-09T18:08:42Z)
• Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization [87.96102461221415]
  We develop an algorithm that provides per-class explainability. In an extensive battery of experiments, we demonstrate the ability of our methods to class-specific visualization.
  arXiv  Detail & Related papers  (2020-12-03T18:48:39Z)
• Region Comparison Network for Interpretable Few-shot Image Classification [97.97902360117368]
  Few-shot image classification has been proposed to effectively use only a limited number of labeled examples to train models for new classes. We propose a metric learning based method named Region Comparison Network (RCN), which is able to reveal how few-shot learning works. We also present a new way to generalize the interpretability from the level of tasks to categories.
  arXiv  Detail & Related papers  (2020-09-08T07:29:05Z)
• Data-driven Meta-set Based Fine-Grained Visual Classification [61.083706396575295]
  We propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition. Specifically, guided by a small amount of clean meta-set, we train a selection net in a meta-learning manner to distinguish in- and out-of-distribution noisy images.
  arXiv  Detail & Related papers  (2020-08-06T03:04:16Z)
• A cellular automata approach to local patterns for texture recognition [3.42658286826597]
  We propose a method for texture descriptors that combines the representation power of complex objects by cellular automata with the known effectiveness of local descriptors in texture analysis. Our proposal outperforms other classical and state-of-the-art approaches, especially in the real-world problem.
  arXiv  Detail & Related papers  (2020-07-15T03:25:51Z)
• Reorganizing local image features with chaotic maps: an application to texture recognition [0.0]
  We propose a chaos-based local descriptor for texture recognition. We map the image into the three-dimensional Euclidean space, iterate a chaotic map over this three-dimensional structure and convert it back to the original image. The performance of our method was verified on the classification of benchmark databases and in the identification of Brazilian plant species based on the texture of the leaf surface.
  arXiv  Detail & Related papers  (2020-07-15T03:15:01Z)
• Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning [86.45526827323954]
  Weakly-supervised semantic segmentation is a challenging task as no pixel-wise label information is provided for training. We propose an iterative algorithm to learn such pairwise relations. We show that the proposed algorithm performs favorably against the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-19T10:32:03Z)

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.