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ABSTRACT Color palette is a critical component of art, design, and lots of applications, providing the basis for organizing and utilizing colors to achieve specific objectives. However, generating color palettes from digital images presents unique challenges due to the complexity of colors in images. This review comprehensively investigates various techniques for generating color palettes from digital images and provides a thorough classification and discussion of these techniques from multiple perspectives. A color space must be selected to generate a color palette, and a generation method must be employed. This paper offers a concise overview of color spaces, an introduction to current palette generation methods, and an analysis of the metrics used to evaluate color differences between palettes. The review encompasses traditional manual methods and computer‐aided automation methods, further categorized as histogram‐based, clustering‐based, and neural network‐based methods. Discussion on the strengths, weaknesses, and applicability of existing methods are presented, and also opportunities for future research to enhance color palette generation from digital images are identified. [ABSTRACT FROM AUTHOR]

Knitted fabric simulation seeks to create lifelike virtual representations of various knitted items like sweaters and socks using mathematical models and advanced simulation techniques. Significant advancements have been achieved in this area. Visual simulations of these fabrics now boast enhanced details, textures, and colors, closely resembling real‐world counterparts. Additionally, physical simulations have improved the movement and deformation aspects of virtual knitted fabrics, enhancing their realism. However, challenges remain. One major concern is the computational demands of these simulations, which can hinder real‐time interactions. Another issue is refining the accuracy of material models, especially when simulating diverse fibers and textures. This paper aims to review the current state of knitted fabric simulation and speculate on its future directions. [ABSTRACT FROM AUTHOR]

Fashion image retrieval is an important branch of image retrieval technology. With the rapid development of online shopping, fashion image retrieval technology has made a breakthrough from text‐based to content‐based. But there is still not a proper deep learning method used for fashion image retrieval. This article proposes a fashion image retrieval framework based on dilated convolutional residual network which consists of two major parts, image feature extraction and feature distance measurement. For image feature extraction, we first extract the shallow features of the input image by a multi‐scale convolutional network, and then develop a novel dilated convolutional residual network to obtain the deep features of the image. Finally, the extracted features are transformed into high‐dimensional features vector by a binary retrieval vector module. For feature distance measurement, we first use PCA to reduce the dimension of the extracted high‐dimensional vectors. Then we propose a mixed distance measurement algorithm combined with cosine distance and Mahalanobis distance to calculate the spatial distance of the feature vectors for similarity ranking, which solves the problems of poor robustness in complex background fashion image retrieval and the inefficiency calculation of Mahalanobis distance. The experimental results show the superiority of our fashion image retrieval framework over existing state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

For digital camera‐based spectra recovery, the spectral reflectance of the object being imaged always needs to be accurately recovered using training samples from available database. Considering the heavy workload when using all samples in database as training samples in practice, a new representative samples selection method is proposed for efficient digital camera‐based spectra recovery based on single RGB image. The representative simulation system is firstly constructed through correlation analysis of spectra recovery results of different systems, and based on the representative simulation system, a few number of representative samples are selected from the database based on minimum of the defined simulate spectra recovery error. The effectiveness of the proposed method is evaluated and compared with existing method. As the results show, the proposed method outperforms the existing methods, and the robustness of the selected representative samples is consistent with the database in practical applications. [ABSTRACT FROM AUTHOR]