Non-parametric scene parsing: Label transfer methods and datasets

Bhowmick, Alexy; Saharia, Sarat; Hazarika, Shyamanta M.

doi:10.1016/j.cviu.2022.103418

Cited by 3 publications

(2 citation statements)

References 185 publications

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“…The non-supervised character of the proposed method also eliminates the risk of human perception and context-driven errors in color analysis. Furthermore, ABANICCO’s potential extends beyond standalone use, and could serve as an initial step in more complex deep learning tasks, such as reducing training time, and improving results in a wide range of tasks, like the introduction of semantic information as organized color data [ 52 , 53 ], sampling and registration [ 54 ], compression [ 55 ], image enhancement and dehazing [ 56 , 57 ], spectral unmixing [ 58 ], and soft-labeling [ 59 ].…”

Section: Results and Discussionmentioning

confidence: 99%

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

Nicolás-Sáenz

Ledezma

Pascau

et al. 2023

Sensors

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Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. The proposed model, “ABANICCO” (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC–NBS color system; its usefulness for image segmentation was tested against state-of-the-art methods. This empirical evaluation provided evidence of ABANICCO’s accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging.

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Section: Results and Discussionmentioning

confidence: 99%

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

Nicolás-Sáenz

Ledezma

Pascau

et al. 2023

Sensors

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“…We expect ABANICCO to be useful not only as a stand-alone application but also as an initial step in more complex tasks in Deep Learning, mainly training and labeling. We believe that our method represents a considerable improvement by providing automatic labels to reduce training time and improve results in a wide range of tasks such as the introduction of semantic information as organized color data [52,53], sampling and registration [54], compression [55], image enhancement and dehazing [56], and soft-labeling [57].…”

Section: Discussionmentioning

confidence: 99%

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

Nicolás-Sáenz¹,

Ledezma²,

Pascau³

et al. 2022

Preprint

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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.

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Constructing Prototype-Based Granular Fuzzy Rules for Scene Classification on Mobile Devices

Rakytyanska,

Prus

2024

Lecture Notes on Data Engineering and Communications Technologies

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Non-parametric scene parsing: Label transfer methods and datasets

Cited by 3 publications

References 185 publications

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

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

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

Constructing Prototype-Based Granular Fuzzy Rules for Scene Classification on Mobile Devices

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