Related papers: ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation

ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation

URL: http://arxiv.org/abs/2211.08460v1
Date: Tue, 15 Nov 2022 19:26:51 GMT
Title: ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation
Authors: Laura Nicol\'as-S\'aenz, Agapito Ledezma, Javier Pascau, Arrate Mu\~noz-Barrutia
Abstract summary: We propose a novel method combining geometric analysis of color theory, fuzzy color spaces, and multi-label systems for the automatic classification of pixels according to 12 standard color categories. We present a robust, unsupervised, unbiased strategy for color naming based on statistics and color theory.
Score: 1.7205106391379026
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In any computer vision task involving color images, a necessary step is classifying pixels according to color and segmenting the respective areas. However, the development of methods able to successfully complete this task has proven challenging, mainly due to the gap between human color perception, linguistic color terms, and digital representation. In this paper, we propose a novel method combining geometric analysis of color theory, fuzzy color spaces, and multi-label systems for the automatic classification of pixels according to 12 standard color categories (Green, Yellow, Light Orange, Deep Orange, Red, Pink, Purple, Ultramarine, Blue, Teal, Brown, and Neutral). Moreover, we present a robust, unsupervised, unbiased strategy for color naming based on statistics and color theory. ABANICCO was tested against the state of the art in color classification and with the standarized ISCC-NBS color system, providing accurate classification and a standard, easily understandable alternative for hue naming recognizable by humans and machines. We expect this solution to become the base to successfully tackle a myriad of problems in all fields of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging.

Related papers

Name Your Colour For the Task: Artificially Discover Colour Naming via Colour Quantisation Transformer [62.75343115345667]
We propose a novel colour quantisation transformer, CQFormer, that quantises colour space while maintaining machine recognition on the quantised images. We observe the consistent evolution pattern between our artificial colour system and basic colour terms across human languages. Our colour quantisation method also offers an efficient quantisation method that effectively compresses the image storage.
arXiv Detail & Related papers (2022-12-07T03:39:18Z)
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)
Learning to Structure an Image with Few Colors and Beyond [59.34619548026885]
We propose a color quantization network, ColorCNN, which learns to structure an image in limited color spaces by minimizing the classification loss. We introduce ColorCNN+, which supports multiple color space size configurations, and addresses the previous issues of poor recognition accuracy and undesirable visual fidelity under large color spaces. For potential applications, we show that ColorCNNs can be used as image compression methods for network recognition.
arXiv Detail & Related papers (2022-08-17T17:59:15Z)
TIC: Text-Guided Image Colorization [24.317541784957285]
We propose a novel deep network that takes two inputs (the grayscale image and the respective encoded text description) and tries to predict the relevant color gamut. As the respective textual descriptions contain color information of the objects present in the scene, the text encoding helps to improve the overall quality of the predicted colors. We have evaluated our proposed model using different metrics and found that it outperforms the state-of-the-art colorization algorithms both qualitatively and quantitatively.
arXiv Detail & Related papers (2022-08-04T18:40:20Z)
Influence of Color Spaces for Deep Learning Image Colorization [2.3705923859070217]
Existing colorization methods rely on different color spaces: RGB, YUV, Lab, etc. In this chapter, we aim to study their influence on the results obtained by training a deep neural network. We compare the results obtained with the same deep neural network architecture with RGB, YUV and Lab color spaces.
arXiv Detail & Related papers (2022-04-06T14:14:07Z)
Assessing The Importance Of Colours For CNNs In Object Recognition [70.70151719764021]
Convolutional neural networks (CNNs) have been shown to exhibit conflicting properties. We demonstrate that CNNs often rely heavily on colour information while making a prediction. We evaluate a model trained with congruent images on congruent, greyscale, and incongruent images.
arXiv Detail & Related papers (2020-12-12T22:55:06Z)
SCGAN: Saliency Map-guided Colorization with Generative Adversarial Network [16.906813829260553]
We propose a fully automatic Saliency Map-guided Colorization with Generative Adversarial Network (SCGAN) framework. It jointly predicts the colorization and saliency map to minimize semantic confusion and color bleeding. Experimental results show that SCGAN can generate more reasonable colorized images than state-of-the-art techniques.
arXiv Detail & Related papers (2020-11-23T13:06:54Z)
Visualizing Color-wise Saliency of Black-Box Image Classification Models [0.0]
A classification result given by an advanced method, including deep learning, is often hard to interpret. We propose MC-RISE (Multi-Color RISE), which is an enhancement of RISE to take color information into account in an explanation. Our method not only shows the saliency of each pixel in a given image as the original RISE does, but the significance of color components of each pixel; a saliency map with color information is useful especially in the domain where the color information matters.
arXiv Detail & Related papers (2020-10-06T04:27:18Z)
Image Colorization: A Survey and Dataset [94.59768013860668]
This article presents a comprehensive survey of state-of-the-art deep learning-based image colorization techniques. It categorizes the existing colorization techniques into seven classes and discusses important factors governing their performance. We perform an extensive experimental evaluation of existing image colorization methods using both existing datasets and our proposed one.
arXiv Detail & Related papers (2020-08-25T01:22:52Z)
Learning to Structure an Image with Few Colors [59.34619548026885]
We propose a color quantization network, ColorCNN, which learns to structure the images from the classification loss in an end-to-end manner. With only a 1-bit color space (i.e., two colors), the proposed network achieves 82.1% top-1 accuracy on the CIFAR10 dataset. For applications, when encoded with PNG, the proposed color quantization shows superiority over other image compression methods in the extremely low bit-rate regime.
arXiv Detail & Related papers (2020-03-17T17:56:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.