Color Image Set Recognition Based on Quaternionic Grassmannians

• URL: http://arxiv.org/abs/2505.23629v2
• Date: Thu, 17 Jul 2025 15:53:12 GMT
• Title: Color Image Set Recognition Based on Quaternionic Grassmannians
• Authors: Xiang Xiang Wang, Tin-Yau Tam,
• Abstract summary: We propose a new method for recognizing color image sets using quaternionic Grassmannians.<n>We provide a formula to calculate the shortest distance between two points on the quaternionic Grassmannian, and use this distance to build a new classification framework.
• Score: 2.447027945847154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new method for recognizing color image sets using quaternionic Grassmannians, which use the power of quaternions to capture color information and represent each color image set as a point on the quaternionic Grassmannian. We provide a direct formula to calculate the shortest distance between two points on the quaternionic Grassmannian, and use this distance to build a new classification framework. Experiments on the ETH-80 benchmark dataset and and the Highway Traffic video dataset show that our method achieves good recognition results. We also discuss some limitations in stability and suggest ways the method can be improved in the future.

Related papers

• Improving analytical color and texture similarity estimation methods for dataset-agnostic person reidentification [0.0]
  It is proposed to analyze and compare colors in CIE-Lab color space using histogram smoothing for noise reduction.<n>A novel pre-configured latent space (LS) supervised autoencoder (SAE) is proposed for texture analysis.<n>The viability of the proposed method is verified by computing rank-1, rank-10, and mAP re-id metrics on Market1501 dataset.
  arXiv  Detail & Related papers  (2024-12-06T14:34:32Z)
• Enhanced Sharp-GAN For Histopathology Image Synthesis [63.845552349914186]
  Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection. We propose a novel approach that enhances the quality of synthetic images by using nuclei topology and contour regularization. The proposed approach outperforms Sharp-GAN in all four image quality metrics on two datasets.
  arXiv  Detail & Related papers  (2023-01-24T17:54:01Z)
• Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer [4.9260675787714]
  Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) We propose cross-modal style transfer (CMST) to leverage large and diverse color imagery datasets. We find that CMST is highly effective for DNN-based detectors.
  arXiv  Detail & Related papers  (2022-12-24T22:38:16Z)
• MIMT: Multi-Illuminant Color Constancy via Multi-Task Local Surface and Light Color Learning [42.72878256074646]
  We introduce a multi-task learning method to discount multiple light colors in a single input image. To have better cues of the local surface/light colors under multiple light color conditions, we design a novel multi-task learning framework. Our model achieves 47.1% improvement compared to a state-of-the-art multi-illuminant color constancy method on a multi-illuminant dataset.
  arXiv  Detail & Related papers  (2022-11-16T09:00:20Z)
• PalGAN: Image Colorization with Palette Generative Adversarial Networks [51.59276436217957]
  We propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. PalGAN outperforms state-of-the-arts in quantitative evaluation and visual comparison, delivering notable diverse, contrastive, and edge-preserving appearances.
  arXiv  Detail & Related papers  (2022-10-20T12:28:31Z)
• Cross-Camera Deep Colorization [10.254243409261898]
  We propose an end-to-end convolutional neural network to align and fuse images from a color-plus-mono dual-camera system. Our method consistently achieves substantial improvements, i.e., around 10dB PSNR gain.
  arXiv  Detail & Related papers  (2022-08-26T11:02:14Z)
• Neural Color Operators for Sequential Image Retouching [62.99812889713773]
  We propose a novel image retouching method by modeling the retouching process as performing a sequence of newly introduced trainable neural color operators. The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar. Our method consistently achieves the best results compared with SOTA methods in both quantitative measures and visual qualities.
  arXiv  Detail & Related papers  (2022-07-17T05:33:19Z)
• Detecting Recolored Image by Spatial Correlation [60.08643417333974]
  Image recoloring is an emerging editing technique that can manipulate the color values of an image to give it a new style. In this paper, we explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring. Our method achieves the state-of-the-art detection accuracy on multiple benchmark datasets and exhibits well generalization for unknown types of recoloring methods.
  arXiv  Detail & Related papers  (2022-04-23T01:54:06Z)
• Semantic keypoint-based pose estimation from single RGB frames [64.80395521735463]
  We present an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. We show that our approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios.
  arXiv  Detail & Related papers  (2022-04-12T15:03:51Z)
• Generative Probabilistic Image Colorization [2.110198946293069]
  We propose a diffusion-based generative process that trains a sequence of probabilistic models to reverse each step of noise corruption. Given a line-drawing image as input, our method suggests multiple candidate colorized images. Our proposed approach performed well not only on color-conditional image generation tasks, but also on some practical image completion and inpainting tasks.
  arXiv  Detail & Related papers  (2021-09-29T16:10:12Z)
• Light Direction and Color Estimation from Single Image with Deep Regression [25.45529007045549]
  We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source.
  arXiv  Detail & Related papers  (2020-09-18T17:33:49Z)
• Probabilistic Color Constancy [88.85103410035929]
  We define a framework for estimating the illumination of a scene by weighting the contribution of different image regions. The proposed method achieves competitive performance, compared to the state-of-the-art, on INTEL-TAU dataset.
  arXiv  Detail & Related papers  (2020-05-06T11:03:05Z)
• UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [81.5490760424213]
  We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
  arXiv  Detail & Related papers  (2020-04-13T04:12:59Z)

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.