Comparative Assessment of Content-Based Face Image Retrieval in Different Color Spaces

Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I1I2I3, HSI, and rgb) by evaluating seven color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB and RGB. Experimental results using 600 FERET color images corresponding to 200 subjects and 456 FRGC (Face Recognition Grand Challenge) color images of 152 subjects show that some color configurations, such as YV in the YUV color space and YI in the YIQ color space, help improve face retrieval performance.

A Study of the Effect of Color Quantization Schemes for Different Color Spaces on Content-based Image Retrieval

International Journal of Computer Applications ◽

10.5120/16843-6699 ◽

2014 ◽

Vol 96 (12) ◽

pp. 1-8

Author(s):

Moheb R.Girgis ◽

Mohammed S. Reda

Keyword(s):

Image Retrieval ◽

Color Spaces ◽

Color Quantization ◽

Farbbasiertes Image Retrieval

10.31237/osf.io/mcp4h ◽

2019 ◽

Author(s):

Thomas Kluth

Keyword(s):

Image Retrieval ◽

Color Spaces ◽

This (German) bachelor thesis discusses to what extent the feature color helps for content-based image retrieval. It analyses the use of different color spaces as well as different spatial segmentations of the image. It seems that color alone is not a very helpful feature for content-based image retrieval.

An Efficient Two-layer based Technique for Content-based Image Retrieval

UHD Journal of Science and Technology ◽

10.21928/uhdjst.v5n1y2021.pp28-40 ◽

2021 ◽

Vol 5 (1) ◽

pp. 28

Author(s):

Fawzi Abdul Azeez Salih ◽

Alan Anwer Abdulla

Keyword(s):

Image Retrieval ◽

Texture Features ◽

Superior Performance ◽

Discrete Wavelet ◽

Color Spaces ◽

Query Image ◽

Current State ◽

Similar Images ◽

The rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain most similar images to the query image. In other words, first step aims to eliminate the most dissimilar images to the query image to reduce the range of search in the dataset of images. In the second layer, the query image is compared to the images obtained in the first layer based on extracting the (texture and color)-based features. The Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) were used as texture features. However, for the color features, three different color spaces were used, namely RGB, HSV, and YCbCr. The color spaces are utilized by calculating the mean and entropy for each channel separately. Corel-1K was used for evaluating the proposed approach. The experimental results prove the superior performance of the proposed concept of two-layer over the current state-of-the-art techniques in terms of precision rate in which achieved 82.15% and 77.27% for the top-10 and top-20, respectively.

Comparative Assessment of the Image Divide and Link Algorithm in Different Color Spaces

Tatra Mountains Mathematical Publications ◽

10.2478/tmmp-2018-0019 ◽

2018 ◽

Vol 72 (1) ◽

pp. 31-41

Author(s):

Carely Guada ◽

Daniel Gómez ◽

J. Tinguaro Rodríguez ◽

Javier Montero

Keyword(s):

Digital Image ◽

Color Space ◽

Comparative Assessment ◽

Dissimilarity Measure ◽

Color Spaces ◽

Spanning Forest ◽

Different Color ◽

Edge Based

Abstract In this paper, a comparative assessment of the Image Divide and Link Algorithm (ID&L) in different color spaces is presented. This, in order to show the significance of choosing a specific color space when the algorithm computes the dissimilarity measure between adjacent pixels. Specifically, the algorithm procedure is based on treating a digital image as a graph, assigning a weight to each edge based on the dissimilarity measure between adjacent pixels. Then, the algorithm constructs a spanning forest through a Kruskal scheme to order the edges successively while partitions are obtained. This process is driven until all the pixels of the image are segmented, that is, there are as many regions as pixels. The results of the algorithm which have been compared with those generated using different color spaces are shown.

A Study of Distance Metrics in Histogram Based Image Retrieval

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v4i3.4205 ◽

2013 ◽

Vol 4 (3) ◽

pp. 821-830 ◽

Cited By ~ 1

Author(s):

Abhijeet Kumar Sinha ◽

K.K. Shukla

Keyword(s):

Image Retrieval ◽

Digital Data ◽

Manhattan Distance ◽

Distance Metrics ◽

Color Spaces ◽

Query Image ◽

Histogram Intersection ◽

Exhaustive Study ◽

There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image Retrieval (CBIR) is certainly the only solution to this problem. Difference between colors of two images can be an important metric to measure their similarity or dissimilarity. Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric.In this study, COREL Database is used for an exhaustive study of various distance metrics on different color spaces. Euclidean distance, Manhattan distance, Histogram Intersection and Vector Cosine Angle distances are used to compare histograms in both RGB and HSV color spaces. So, a total of 8 distance metrics for comparison of images for the sake of CBIR are discussed in this work.