Estimating Appearance Models for Image Segmentation via Tensor Factorization

• URL: http://arxiv.org/abs/2208.07853v2
• Date: Wed, 15 Nov 2023 15:18:11 GMT
• Title: Estimating Appearance Models for Image Segmentation via Tensor Factorization
• Authors: Jeova Farias Sales Rocha Neto
• Abstract summary: We propose a new approach to directly estimate appearance models from the image without prior information on the underlying segmentation. Our method uses local high order color statistics from the image as an input to tensor factorization-based estimator for latent variable models. This approach is able to estimate models in multiregion images and automatically output the regions proportions without prior user interaction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image Segmentation is one of the core tasks in Computer Vision and solving it often depends on modeling the image appearance data via the color distributions of each it its constituent regions. Whereas many segmentation algorithms handle the appearance models dependence using alternation or implicit methods, we propose here a new approach to directly estimate them from the image without prior information on the underlying segmentation. Our method uses local high order color statistics from the image as an input to tensor factorization-based estimator for latent variable models. This approach is able to estimate models in multiregion images and automatically output the regions proportions without prior user interaction, overcoming the drawbacks from a prior attempt to this problem. We also demonstrate the performance of our proposed method in many challenging synthetic and real imaging scenarios and show that it leads to an efficient segmentation algorithm.

Related papers

• Pixel-Inconsistency Modeling for Image Manipulation Localization [63.54342601757723]
  Digital image forensics plays a crucial role in image authentication and manipulation localization. This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
  arXiv  Detail & Related papers  (2023-09-30T02:54:51Z)
• Interpretable Small Training Set Image Segmentation Network Originated from Multi-Grid Variational Model [5.283735137946097]
  Deep learning (DL) methods have been proposed and widely used for image segmentation. DL methods usually require a large amount of manually segmented data as training data and suffer from poor interpretability. In this paper, we replace the hand-crafted regularity term in the MS model with a data adaptive generalized learnable regularity term.
  arXiv  Detail & Related papers  (2023-06-25T02:34:34Z)
• A Generalist Framework for Panoptic Segmentation of Images and Videos [61.61453194912186]
  We formulate panoptic segmentation as a discrete data generation problem, without relying on inductive bias of the task. A diffusion model is proposed to model panoptic masks, with a simple architecture and generic loss function. Our method is capable of modeling video (in a streaming setting) and thereby learns to track object instances automatically.
  arXiv  Detail & Related papers  (2022-10-12T16:18:25Z)
• Saliency-Driven Active Contour Model for Image Segmentation [2.8348950186890467]
  We propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models.
  arXiv  Detail & Related papers  (2022-05-23T06:02:52Z)
• Unsupervised Deep Learning Meets Chan-Vese Model [77.24463525356566]
  We propose an unsupervised image segmentation approach that integrates the Chan-Vese (CV) model with deep neural networks. Our basic idea is to apply a deep neural network that maps the image into a latent space to alleviate the violation of the piecewise constant assumption in image space.
  arXiv  Detail & Related papers  (2022-04-14T13:23:57Z)
• Diffusion Models for Implicit Image Segmentation Ensembles [1.444701913511243]
  We present a novel semantic segmentation method based on diffusion models. By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images. Compared to state-of-the-art segmentation models, our approach yields good segmentation results and, additionally, meaningful uncertainty maps.
  arXiv  Detail & Related papers  (2021-12-06T16:28:15Z)
• Meta Internal Learning [88.68276505511922]
  Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. We propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively. Our results show that the models obtained are as suitable as single-image GANs for many common image applications.
  arXiv  Detail & Related papers  (2021-10-06T16:27:38Z)
• Understanding Gender and Racial Disparities in Image Recognition Models [0.0]
  We investigate a multi-label softmax loss with cross-entropy as the loss function instead of a binary cross-entropy on a multi-label classification problem. We use the MR2 dataset to evaluate the fairness in the model outcomes and try to interpret the mistakes by looking at model activations and suggest possible fixes.
  arXiv  Detail & Related papers  (2021-07-20T01:05:31Z)
• Direct Estimation of Appearance Models for Segmentation [0.0]
  We describe a novel approach for estimating appearance models directly from an image. Our approach is based on algebraic expressions that relate local image statistics to the appearance models of spatially coherent regions. We present experimental results that demonstrate the proposed methods work well in practice and lead to effective image segmentation algorithms.
  arXiv  Detail & Related papers  (2021-02-22T15:50:39Z)
• Self-supervised Human Detection and Segmentation via Multi-view Consensus [116.92405645348185]
  We propose a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training. We show that our approach outperforms state-of-the-art self-supervised person detection and segmentation techniques on images that visually depart from those of standard benchmarks.
  arXiv  Detail & Related papers  (2020-12-09T15:47:21Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)

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.