# An Enhanced Triadic Color Scheme for Content-Based Image Retrieval

Authors: S. K. B. Sangeetha; Sandeep Kumar Mathivanan; Thanapal Pandi; K. Arivu selvan; Prabhu Jayagopal; Gemmachis Teshite Dalu
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The complexity of multimedia content, particularly images, has risen dramatically in recent years, and millions of images are shared on social media every day. Finding or retrieving an appropriate image is becoming more difficult due to the increase in the volume of shared and archived multimedia data. Any image retrieval model must, at a bare minimum, locate and classify images that are visually related to the user’s query. The vast majority of Internet search engines employ text algorithms that fetch images using captions as input. Even though there is a lot of study being done to increase the effectiveness of automatic image annotation, retrieval errors can occur due to differences in visual perception. Content-based image retrieval (CBIR) addresses the aforementioned issue because visual analysis of the content is included in the query image. On the other hand, feature extraction is significantly challenging because of semantic gap. This work proposes a strategy for effective retrieval in similarity images using the triadic color scheme RGB, YCbCr, andL∗a∗b∗ based on reranking. We want to increase image similarity and encourage more relevant reranking. As a result of the findings, it can be concluded that a triadic color scheme improves precision by 5% more dramatically than existing schemes and also efficiently improves retrieved results while reducing user effort.


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## Body

## 1. Introduction

Recent developments in big data technology have produced a sizable number of image databases. A capable visual search tool is required for all of these image libraries. There are two methods for conducting a search. Keywords are used to annotate images in text-based image retrieval [1].This approach has a number of drawbacks:(1)
It is impossible to manually annotate large databases(2)
The technique must be annotated by the end user, making it sensitive to human perception(3)
Only one language is covered by these annotationsTo address the above challenges with multimedia research, CBIR analyses large-scale images employing local and global features in digital image processing [2]. There are several ways in which this method differs from text-based image retrieval systems. The CBIR system’s most crucial element is feature extraction [3]. Because color extraction is usually straightforward and retrieval performance is quite high, it is frequently used in CBIR systems [4]. There are yet to be published complete and accurate definition of form features. The Fourier descriptor, aspect ratio, and circularity are all common ways for acquiring geometric form information. Furthermore, three texture-description approaches are used: statistical, structural, and spectral methods [5, 6].Image processing applications including image compression, edge detection, and picture retrieval have all exploited wavelet features, a sort of texture. A technique for locating and eliminating pointless and unnecessary feature components is feature selection [7, 8]. Low temporal complexity and excellent system accuracy could be attained as a result. For a variety of applications, including face identification, data mining and pattern recognition, automatic speaker verification systems, and image processing applications [9], a number of feature selection algorithms have been developed. Finding similar images using visual cues including shape, color, texture, and edge detection is the primary objective of CBIR [10, 11]. The primary flaw is that images with low-level attributes may differ from the queried image in the user’s perception of semantic locations. The major purpose of this research is to integrate color features in order to obtain images that are more similar. Color images can be represented as RGB or indexed images. The RGB information of an image is found using color moments. RGB, YCbCr, and L∗a∗b are some of the color methods utilized for a color histogram. Color approaches employed in image processing are quite sensitive to these strategies [12].The fundamental color scheme is YCbCr, which is expressed as one color brightness and two different colors in photos and movies. Brightness (luma), minus luma (B-Y), and red minus luma (R-Y) are all represented by the lettersY, Cb, and Cr, respectively [13]. Cb and Cr, two color-difference components, are used to hold color data. Differences between the blue and reference values are denoted by the letters Cb, while the differences between the red and reference values are denoted by the letters Cr [14]. The L∗a∗b∗ method for color space translation separates gray-scale data from color data like red, green, and blue [15, 16]. Table 1 shows the various conventional methods along with its performance metric.Table 1
Conventional methods comparison.
ReferenceCBIR classifierMethodAccuracy (%)[17]Random forestMultikey image hashing72[18]Support vector machineTwo-step strategy81[19]Naive bayesStatistical approach65[20]K nearest neighborDiscrete wavelet transform68.7[21]Fuzzy ruleDiscrete wavelet transform84L∗a∗b∗ has two main processes as follows:(1)
The color channel’s gray-scale information is more clearly separated. The characterL∗ denotes more gray-scale information. The characters a∗ and b∗ provide color information.(2)
TheL∗a∗b∗ generated by the Euclidean distance difference between colors is perceptually consistent.The main contributions of the study are as follows:(1)
To construct a triadic color scheme, and this is a mixture of low-level elements such as RGB, YCbCr, andL∗a∗b∗ and are defined as visual information content(2)
We proposed a new framework that can be utilized with a variety of primary color approaches to capture and classify integrated heterogeneous image data.


## 2. System Model

The proposed method is hybrid features extracted from an individual RGB, YCbCr, andL∗a∗b∗. To decrease the significant feature vector length of a recommended descriptor, the hybrid features were extracted from color and use gray level and color information from a color map to compute numerous color properties. A color histogram is made using RGB, YCbCr, and L∗a∗b, among other digital color approaches. These tactics have a very high sensitivity of color recognition cells in human visual information. RGB to YCbCr and RGB to L∗a∗b conversions are used in digital picture processing. For RGB images of type unit 8 and unit 16, the range of values is [0, 255] and [0, 65535]. We present a set of integrated color attributes in this paper that can be used to generate more relevant images. Feature extraction methods identify the most important features and simplify image retrieval computations. The combined color channel includes RGB, YCbCr, and L∗a∗b∗ features, which are based on K-means classification and then reranking features. Certain images have greater color traits, while others have more sensitive image features. The RGB, YCbCr, L∗a∗b∗, and optimal color attributes are integrated to make image retrieval easier.In order to increase image retrieval rate and simplify computation of image retrieval methods, we conducted a number of analyses and comparisons on integrated color information. By deriving ideal combination features from original features, the retrieval rate is enhanced. RGB, YCbCr, andL∗a∗b∗ are triadic color schemes that use gray level and color information from a color map to compute numerous color properties. Our goal is to make reranking picture results more relevant by maximizing image retrieval of mixed color features. The proposed color algorithms can be applied to medical imaging, face recognition, and form matching, to name a few. Figure 1 depicts the framework for combined color approaches.Figure 1
Triadic combined color framework.


### 2.1. Feature Extraction

Extraction of visual features to a considerable extent in order to improve categorization is known as feature extraction. The CBIR uses a number of different feature extraction approaches. Our analysis is based on a mix of color and frequency prominent properties extracted from the input images.


### 2.2. Procedure

#### 2.2.1. RGB Color Space

The RGB color model is additive; therefore, the following can be used:(1)
Magenta = red + blue(2)
Red plus green = yellow(3)
Cyan is made up of two colors: blue and greenColor auto-correlogram features are the likelihood of color Qi pixels in a query image, and they are calculated in various sections of the image. It locates spatial information, which is then utilized to include color and histogram data. The auto-correlogram of image I with distanced for color Qi is defined as(1)γQidI=XrX1−X2=d,X2∈IQi',Xi∈IQi.Equation (1) shows the autocorrelation of color adjusted fluctuation with distance space information. In the auto-correlogram, color information and spatial information are integrated. In the same way, each pixel in the image might travel through all of its neighbors. The computation complexity is calculated as(2)Od∗n2,where n is the number of neighbor pixels, and d is defined as pixel distance.


### 2.3. YCbCr Color Space

The basic color approach is YCbCr, which is included in images and videos and is expressed as one color luminance and two color-difference signals. The lettersY, Cb, and Cr stand for brightness (luma), minus luma (B-Y), and red minus luma (R-Y). Two color components, Cb and Cr, are used to store color information. Cb indicates the difference between the blue and the reference value, while Cr indicates the difference between the red and the reference value.(3)Y=16+65.74R/256+129.06G/256+25.06B/256,Cb=128+37.95R256−74.49G256+112.44B256,Cr=128+112.44R256−94.15G256+18.29B256.


### 2.4.L∗a∗b∗ Color Space

Gray-scale information is represented using theL∗a∗b∗ approach. More gray-scale information is indicated by the letter L∗. Both a∗ and b∗ are used to indicate color information. The L∗a∗b∗ formula is dubbed perceptually uniform since it is designed by the Euclidean distance difference between colors:(4)L∗=116RRn1/3−16,forRRn>0.009,L∗=903.3RRn1/3,forRRn>0009,a∗=500fSSn−fRRn,b∗=200fSSn−fTTn.Color feature extraction and concatenation were conducted on each of the original color image channels, such as theR, G, and B channels. RGB, YCbCr, and L∗a∗b∗ are applied to the color image channels. Each channel’s distance is calculated separately, and the best values are used. To match a completely RGB, YCbCr, and L∗a∗b∗ image, the Euclidean distance is employed. This method is applied to all color-characteristics channels. Single feature extraction does not provide optimal performance, which is one of the main reasons for missing the proper feature values. The combined RGB, YCbCr, and L∗a∗b∗ three channel characteristics are a great compromise for long-range tasks because they are exceptionally fast to retrieve.


## 2.1. Feature Extraction

Extraction of visual features to a considerable extent in order to improve categorization is known as feature extraction. The CBIR uses a number of different feature extraction approaches. Our analysis is based on a mix of color and frequency prominent properties extracted from the input images.


## 2.2. Procedure

### 2.2.1. RGB Color Space

The RGB color model is additive; therefore, the following can be used:(1)
Magenta = red + blue(2)
Red plus green = yellow(3)
Cyan is made up of two colors: blue and greenColor auto-correlogram features are the likelihood of color Qi pixels in a query image, and they are calculated in various sections of the image. It locates spatial information, which is then utilized to include color and histogram data. The auto-correlogram of image I with distanced for color Qi is defined as(1)γQidI=XrX1−X2=d,X2∈IQi',Xi∈IQi.Equation (1) shows the autocorrelation of color adjusted fluctuation with distance space information. In the auto-correlogram, color information and spatial information are integrated. In the same way, each pixel in the image might travel through all of its neighbors. The computation complexity is calculated as(2)Od∗n2,where n is the number of neighbor pixels, and d is defined as pixel distance.


## 2.2.1. RGB Color Space

The RGB color model is additive; therefore, the following can be used:(1)
Magenta = red + blue(2)
Red plus green = yellow(3)
Cyan is made up of two colors: blue and greenColor auto-correlogram features are the likelihood of color Qi pixels in a query image, and they are calculated in various sections of the image. It locates spatial information, which is then utilized to include color and histogram data. The auto-correlogram of image I with distanced for color Qi is defined as(1)γQidI=XrX1−X2=d,X2∈IQi',Xi∈IQi.Equation (1) shows the autocorrelation of color adjusted fluctuation with distance space information. In the auto-correlogram, color information and spatial information are integrated. In the same way, each pixel in the image might travel through all of its neighbors. The computation complexity is calculated as(2)Od∗n2,where n is the number of neighbor pixels, and d is defined as pixel distance.


## 2.3. YCbCr Color Space

The basic color approach is YCbCr, which is included in images and videos and is expressed as one color luminance and two color-difference signals. The lettersY, Cb, and Cr stand for brightness (luma), minus luma (B-Y), and red minus luma (R-Y). Two color components, Cb and Cr, are used to store color information. Cb indicates the difference between the blue and the reference value, while Cr indicates the difference between the red and the reference value.(3)Y=16+65.74R/256+129.06G/256+25.06B/256,Cb=128+37.95R256−74.49G256+112.44B256,Cr=128+112.44R256−94.15G256+18.29B256.


## 2.4.L∗a∗b∗ Color Space

Gray-scale information is represented using theL∗a∗b∗ approach. More gray-scale information is indicated by the letter L∗. Both a∗ and b∗ are used to indicate color information. The L∗a∗b∗ formula is dubbed perceptually uniform since it is designed by the Euclidean distance difference between colors:(4)L∗=116RRn1/3−16,forRRn>0.009,L∗=903.3RRn1/3,forRRn>0009,a∗=500fSSn−fRRn,b∗=200fSSn−fTTn.Color feature extraction and concatenation were conducted on each of the original color image channels, such as theR, G, and B channels. RGB, YCbCr, and L∗a∗b∗ are applied to the color image channels. Each channel’s distance is calculated separately, and the best values are used. To match a completely RGB, YCbCr, and L∗a∗b∗ image, the Euclidean distance is employed. This method is applied to all color-characteristics channels. Single feature extraction does not provide optimal performance, which is one of the main reasons for missing the proper feature values. The combined RGB, YCbCr, and L∗a∗b∗ three channel characteristics are a great compromise for long-range tasks because they are exceptionally fast to retrieve.


## 3. Ranking Procedure and Metrics

Algorithm 1: Triadic color procedure.
(1)
Choose the most important inquiry images(2)
The input image is in RGB color space(3)
Detection of important objects(4)
Utilize the segmentation methods(5)
Look for the detected object (R, G, and B)(6)
Calculate the correlogram for each RGB-based color feature extraction frame using (1)(7)
Apply YCbCr techniques. For each YCbCr-based color feature extraction image channel, calculate the mean and standard deviation(8)
Use theL∗a∗b∗ methods(9)
Construct the final feature vector (auto-correlogram,L∗a∗b∗ for color moments and YCbCr for color moments) that will be used to represent the image with numerical values(10)
Utilize the C-means algorithm to group the database’s image content(11)
Using Euclidean Distance, calculate the distance between the input image and each cluster’s centroid, and then apply reranking methods to find the shortest distance(12)
Returns the topn images


### 3.1. Distance Metrics

There are two points,q and t, in the Euclidean distance. The database images are identified by the letters t and q, respectively. At points t = (t1, t2, t3, t4, tn), q = (q1, q2, q3, q4, …, qn), measure the Euclidean distance. The reranking algorithm is used after the Euclidean distance.


## 3.1. Distance Metrics

There are two points,q and t, in the Euclidean distance. The database images are identified by the letters t and q, respectively. At points t = (t1, t2, t3, t4, tn), q = (q1, q2, q3, q4, …, qn), measure the Euclidean distance. The reranking algorithm is used after the Euclidean distance.


## 4. Experimental Evaluation and Analysis

CBIR image datasets include the Corel Collection, Wang Collection, ZuBuD, Coil-100, UW, INRIA Holiday, DP challenge, and Google Flickr, to name a few. Corel databases were used in most of the input. The experimental databases used are listed in Table2 and are taken from Kaggle website. The performance of the proposed technique is measured in terms of precision, recall, and accuracy. When compared to other similar color approaches that are already in use, our combined color feature approach outperforms them.Table 2
Datasets.
DatabaseNo of İmagesTest İmage sizeCorel10000200∗140Wang1000256∗256ZuBuD1000400∗400Coil-100500192∗192Holiday1200640∗480Color images are used to populate entire databases. Humans, animals, flowers, food, and other objects are included in the database. WANG Datasets, Holidays Datasets, and other CBIR approaches rely heavily on them. When compared to several database images, the retrieval result was high. Over 50 image query tests are put to the test using the approaches we have proposed. These tests assist us in determining the outcome and level of achievement. Finally, we used 80% datasets for training and 20% datasets for testing in our Corel database experiment.


### 4.1. Performance Analysis

Three important metrics are analyzed for performance evaluation. The measure values where its efficiency and accuracy are evaluated over the Corel Database using precision (Pr), recall (Re), andF-score (Fs) using equations (5) to (7):(5)Precision=Relevant images countTotal images count,(6)Recall=Relevant images fetchedTotal relevant images,(7)F=2PRP+R=21/R−1/P.The proposed method’s overall performance evaluation has shown to be satisfactory. Figure2 displays the outcomes of finding the top images. The triadic color method, also known as the hybrid method, explains how to get an upgraded top comparable image in the right order. Figure 3 depicts an image performance metric comparison, whereas Figure 4 depicts a dataset performance metric comparison. When compared to a current method, the suggested method significantly increases the average value of the performance metrics, as shown in Figure 5.Figure 2
Top most images retrieved.Figure 3
Image-comparison analysis.Figure 4
Dataset-comparison analysis.Figure 5
Existing schemes vs proposed scheme-comparison analysis.From Figure3, it can be observed that the tire image is recognized with 85% accuracy which is higher than other images. Guitar is the least identified image with 78% accuracy. Chair and tire recognizing accuracy is almost the maximum difference by 1%. F-score for airplane and chair equals 70%. The comparison analysis for various datasets is depicted in Figure 4. The accuracy of the Corel Dataset is 88% in comparison with other datasets. Coil-100 dataset gives the lowest F-score 86% which is 7–8% lower than the Corel dataset. From Figure 5, it is shown that image partitioning has the lowest accuracy of 83% and highest accuracy of 93% in comparison with other methods.


## 4.1. Performance Analysis

Three important metrics are analyzed for performance evaluation. The measure values where its efficiency and accuracy are evaluated over the Corel Database using precision (Pr), recall (Re), andF-score (Fs) using equations (5) to (7):(5)Precision=Relevant images countTotal images count,(6)Recall=Relevant images fetchedTotal relevant images,(7)F=2PRP+R=21/R−1/P.The proposed method’s overall performance evaluation has shown to be satisfactory. Figure2 displays the outcomes of finding the top images. The triadic color method, also known as the hybrid method, explains how to get an upgraded top comparable image in the right order. Figure 3 depicts an image performance metric comparison, whereas Figure 4 depicts a dataset performance metric comparison. When compared to a current method, the suggested method significantly increases the average value of the performance metrics, as shown in Figure 5.Figure 2
Top most images retrieved.Figure 3
Image-comparison analysis.Figure 4
Dataset-comparison analysis.Figure 5
Existing schemes vs proposed scheme-comparison analysis.From Figure3, it can be observed that the tire image is recognized with 85% accuracy which is higher than other images. Guitar is the least identified image with 78% accuracy. Chair and tire recognizing accuracy is almost the maximum difference by 1%. F-score for airplane and chair equals 70%. The comparison analysis for various datasets is depicted in Figure 4. The accuracy of the Corel Dataset is 88% in comparison with other datasets. Coil-100 dataset gives the lowest F-score 86% which is 7–8% lower than the Corel dataset. From Figure 5, it is shown that image partitioning has the lowest accuracy of 83% and highest accuracy of 93% in comparison with other methods.


## 5. Conclusion

In this study, a novel model of combined color features is presented that computes distinct color features from color information with a limited number of chosen pixels, making it computationally appealing. The suggested method performed better than earlier detectors and was also compared to other color-characteristics techniques, such as RGB, YCbCr, andL∗a∗b∗, which are less noise-sensitive and result in more comparable images. The Corel Dataset was used to test this method, and the results showed that most of the images had issues. The results of the evaluation revealed that the suggested triadic color features method outperforms the existing method in terms of accuracy.



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