Image Retrieval Using Spatial Dominant Color Descriptor

Rejeb, Imen Ben; Ouni, Sonia; Zagrouba, Ezzeddine

doi:10.1109/aiccsa.2017.127

Cited by 6 publications

(7 citation statements)

References 25 publications

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“…As a result, the chromatic details are not straightforwardly related to the application. The CDH also takes into account the composition of the area without image fragmentation, learning processes, or clustering implementation [ 14 ]. The algorithm implemented for CDH is described by the flowchart as shown in Figure 3 and briefly explained in the following subsections.…”

Section: Methodsmentioning

confidence: 99%

“…Talib et al [ 13 ] proposed the Spatial Dominant Color Descriptor (SDCD) as a top-down descriptor. Rejeb et al [ 14 ] presented the approach of CDH which counts under various backdrops, the perceptually consistent color difference between two points in Lab color space. Liu and Yang [ 15 ] proposed the innovative approach to complete the retrieval process employed on color, texture, and shape.…”

Section: Introductionmentioning

confidence: 99%

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Image Retrieval Using Different Distance Methods and Color Difference Histogram Descriptor for Human Healthcare

Chugh

Gupta

Garg

et al. 2022

Journal of Healthcare Engineering

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As multimedia technology is developing and growing these days, the use of an enormous number of images and its datasets is likewise expanding at a quick rate. Such datasets can be utilized for the purpose of image retrieval. This research focuses on extraction of similar images established on its different features for the image retrieval purpose from huge dataset of images. In this paper initially, the query image is searched within the available dataset and, then, the color difference histogram (CDH) descriptor is employed to retrieve the images from database. The basic characteristic of CDH is that it counts the color difference stuck among two distinct labels in the L ∗ a ∗ b ∗ color space. This method is experimented on random images used for various medical purposes. Various unlike features of an image are extracted via different distance methods. The precision rate, recall rate, and F-measure are all used to evaluate the system’s performance. Comparative analysis in terms of F-measure is also made to check for the best distance method used for retrieval of images.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Retrieval Using Different Distance Methods and Color Difference Histogram Descriptor for Human Healthcare

Chugh

Gupta

Garg

et al. 2022

Journal of Healthcare Engineering

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“…In paper [26], a new semantic feature extracted from dominant colors (weight for each DC) is proposed. A top-down descriptor was proposed called the Spatial Dominant Color Descriptor (SDCD) in paper [27]. In the extraction of dominant colors, a dynamic quantization by Gaussian Mixture Models (GMMs) was adopted.…”

Section: Related Workmentioning

confidence: 99%

“…In dominant color descriptor (DCD), the color space, HSV, is divided into partitions known as course partitions. Each partition has two main components, including the partition center and percentage which are computed as [27]: C (X , X , X )…”

Section: Feature Extraction Of Dominant Color Descriptormentioning

confidence: 99%

Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval

et al. 2020

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The rapidly increasing number of digital images requires effective retrieval. Meanwhile, the dominant color descriptor has been widely used in image processing. Due to the influence of lighting and other factors, the same color in nature may have some different changes. The human eye is usually more sensitive to zones of consistent color, often identifying objects by zones of consistency. Therefore, the proposed method in this paper first applies the texton template to detect and extract the consistent zone of an image, and calculates the dominant color descriptor feature on the pixels in this consistent zone. Besides, the translation and rotation invariance of the Hu moments feature is applied to extract the shape information in the same consistent zone of the image. Finally, the combination of the dominant dolor descriptor and the Hu moments is used for content-based image retrieval. The algorithm proposed in this paper is tested on three data sets: Corel-1k, Corel-5k and Corel-10k, and the experimental results show that it is superior to the current content-based image retrieval methods.

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“…However, so far, little attention has been paid to developing an algorithm for extracting the most prominent colors in the image. Based on their working principle the color-extracting methods can be divided into four main categories: histogram-based, 28,29 clusteringbased, 30,31 segmentation-based, 32,33 and data-driven methods. [34][35][36] Unfortunately, these methods do not always guarantee the extraction of the most prominent or visible colors of the image and have several limitations, such as the extraction of multiple perceptually similar colors or inability to detect colors in smaller regions.…”

mentioning

confidence: 99%

Development of a machine learning model for extracting image prominent colors

Weingerl

Hladnik

Javoršek

2020

Color Research & Application

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Although an image can consist of myriad different colors, due to the limitations of the human visual system and the mechanism of selective visual attention, only a limited number of them are prominent, that is, they stand out or are noticeable at first sight. In this article, a framework for building a model for extracting image prominent colors based on machine learning is presented. The model is learned on human‐extracted themes of prominent colors and uses numerous features, which were defined based on the properties of human visual system. For the purpose of this study, we constructed a database of images with their associated human‐extracted themes of prominent colors, which are open to the public and available to other researchers. The analysis of observers' data shows a high interobserver agreement on prominent color categories as well as high diversity of prominent colors. According to our results, the most influential factors on the perception of prominent colors are associated with color coverage (which should be adjusted with a saliency map), color properties—lightness and chroma, and diversity of colors. To the best of our knowledge, this is the first study on image prominent colors and first attempt to extract them with a model trained on the real data. The presented model has a high practical importance, since it can be used for extracting image colors in different scenarios, for example, for automatic color design, image categorization, as a descriptor in content‐based image retrieval, and image content analysis frameworks.

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Image Retrieval Using Spatial Dominant Color Descriptor

Cited by 6 publications

References 25 publications

Image Retrieval Using Different Distance Methods and Color Difference Histogram Descriptor for Human Healthcare

Image Retrieval Using Different Distance Methods and Color Difference Histogram Descriptor for Human Healthcare

Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval

Development of a machine learning model for extracting image prominent colors

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