Hybrid Color Space Research Articles

Enhancing of polyp image segmentation in colonoscopy images: a comprehensive approach using modified UNet, hybrid color space, and ensemble learning

Enhancing of polyp image segmentation in colonoscopy images: a comprehensive approach using modified UNet, hybrid color space, and ensemble learning

Robust And Discriminant Local Color Pattern (RADLCP): A novel color descriptor for face recognition

In [1] Karanwal et al. introduced the novel color descriptor in Face Recognition (FR) called as Fused Local Color Pattern (FLCP). In FLCP, the RGB color format is utilized for extracting features. From R, G and B channels, the MRELBP-NI, 6 × 6 MB-LBP and RD-LBP are imposed for feature extraction and then all are integrated to form the FLCP size. FLCP beats the accuracy of various methods. The one major shortcoming observed in [1] is that the basic format RGB is used for extracting features. Literature suggests that other hybrid formats achieves better recognition rates than RGB. Motivated from literature, the proposed work uses the hybrid color space format RCrQ for feature extraction. In this format R channel is taken from RGB, Cr channel is taken from YCbCr and Q channel is taken from YIQ. On R channel, MRELBP-NI is imposed for extracting features, On Cr channel 6 × 6 MB-LBP is imposed and on Q channel RD-LBP is imposed for extracting features. Then all channel features are joined to build the robust and discriminant feature called as Robust And Discriminant Local Color Pattern (RADLCP). Compression and matching is assisted from PCA and SVMs. For evaluating results GT face dataset is used. Results proves the potency of RADLCP in contrast to gray scale based implemented descriptors. RADLCP also beats the results of FLCP. Several literature techniques are also outclassed by RADLCP. For evaluating all the results MATLAB R2021a is used.

An iterative algorithm for color space optimization on image segmentation

This paper proposes, a novel hybrid color component (HCC) issued from amounts number of color space with iterative manner, in fact traditional images obtained by RGB sensor weren’t the effective way in image processing applications, for this purpose we have propose a supervised algorithm to substitute RGB level by hybrid and suitable color space at the aim to make well representation of the handled amounts of data, this step is extremely important because the obtained results it will be injected in many future studies like tracking, classification, steganography and cryptography. The second part of this paper consists to segment image coded in hybrid color space already selected, the used algorithm is inspired from kernel function where statistical distribution was used to model background and Bayes rule to make decision of the membership of each pixel, in this research topics we have extended this algorithm in the aim to improve compactness of these distribution. Cauchy background modeling and subtraction is used, and shows the high accuracy of automatic player detection.

Color face recognition using normalized-discriminant hybrid color space and quaternion moment vector features

The Zernike and pseudo-Zernike moments (ZMs/PZMs) have been found useful for a variety of applications requiring feature extraction due to their favorable properties, such as low information redundancy, rotational invariance, higher noise resilience and extendibility to the color space. In this paper, we propose an approach for color face recognition based on Zernike/pseudo-Zernike quaternion moment vector (QMV) features and a novel normalized-discriminant hybrid color space. The proposed XnSBr color space is composed by taking Xn from the normalized-XYZ, S from HSV and Br from the discriminant RGB-r color spaces to capture the features efficiently. In addition, we propose the use of quaternion vector distance (QVD) similarity measure for the QMV features in order to enhance the recognition accuracy. The exhaustive comparative performance analyses with the state-of-the-art approaches in the different color spaces demonstrate the superiority of the proposed approach in terms of accuracy and speed.

A Hybrid Color Space for Skin Recognition for Real-Time Applications

Color space conversion utilized by many researchers in order to enhance skin recognition performance by projecting the skin color cluster to a more distinctive distribution. In spite of the substantial research effort in this area, finding a suitable color space for face and skin recognition is still an unsolved issue. Deviation of skin tone under different lighting condition, dissimilarity of skin color among different ethnics and races, various camera sensors characteristics, presence of skin-like color objects in image background and variation of skin color tone among different body limbs are among the major challenges in skin recognition. Majority of these challenges are expected to be mitigated through color space conversion. This paper proposes a new hybrid color space by applying Principal Component Analysis technique to skin color cluster in ten existing conventional color spaces including RGB, YCbCr , YUV, nRGB, i1i2i3, YIQ, XYZ, YPbPr , YES, YCgCr . The proposed hybrid color space which termed P1P2P3 consist of the three major Principal Components of these conventional color spaces components. Using Algebraic simplification these principal component has been reformulated in terms of RGB color space. Parametric pixel wised skin detection techniques have been employed in order to evaluate the proposed color space effect on skin detection performance. Three popular supervised classifiers including Multilayer Perceptron, Support Vector Machine and Random Forest has been employed to generate a parametric model of skin color cluster using the proposed color space. Experiment results shows the proposed hybrid color space P1P2P3 with F -score and False Positive Rate 0.960 and 0.041 respectively performed better than the existing conventional color spaces in term of pixel wised skin recognition.

A hybrid color space for skin detection using genetic algorithm heuristic search and principal component analysis technique.

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.

Color Face Recognition Based on Steerable Pyramid Transform and Extreme Learning Machines

This paper presents a novel color face recognition algorithm by means of fusing color and local information. The proposed algorithm fuses the multiple features derived from different color spaces. Multiorientation and multiscale information relating to the color face features are extracted by applying Steerable Pyramid Transform (SPT) to the local face regions. In this paper, the new three hybrid color spaces, YSCr, Z n SCr, and B n SCr, are firstly constructed using the Cb and Cr component images of the Y Cb Cr color space, the S color component of the HSV color spaces, and the Z n and B n color components of the normalized XYZ color space. Secondly, the color component face images are partitioned into the local patches. Thirdly, SPT is applied to local face regions and some statistical features are extracted. Fourthly, all features are fused according to decision fusion frame and the combinations of Extreme Learning Machines classifiers are applied to achieve color face recognition with fast and high correctness. The experiments show that the proposed Local Color Steerable Pyramid Transform (LCSPT) face recognition algorithm improves seriously face recognition performance by using the new color spaces compared to the conventional and some hybrid ones. Furthermore, it achieves faster recognition compared with state-of-the-art studies.

Fuzzy segmentation of video shots using hybrid color spaces and motion information

Video segmentation can be defined as the process of partitioning video into spatio-temporal objects that are homogeneous in some feature space, with the choice of features being very important to the success of the segmentation process. Fuzzy segmentation is a semi-automatic region-growing segmentation algorithm that assigns to each element in an image a grade of membership in an object. In this paper, we propose an extension of the multi-object fuzzy segmentation algorithm to segment pre-acquired color video shots. The color features are selected from channels belonging to different color models using two different heuristics: one that uses the correlations between the color channels to maximize the amount of information used in the segmentation process, and one that chooses the color channels based on the separation of the clusters formed by the seed spels for all possible color spaces. Motion information is also incorporated into the segmentation process by making use of dense optical flow maps. We performed experiments on synthetic videos, with and without noise, as well as on some real videos. The experiments show promising results, with the segmentations of real videos produced using hybrid color spaces being more accurate than the ones produced using three other color models. We also show that our method compares favorably to a state-of-the art video segmentation algorithm.

Objects Detection by Singular Value Decomposition Technique in Hybrid Color Space: Application to Football Images

In this paper, we present an improvement non-parametric background modeling and foreground segmentation. This method is important; it gives the hand to check many states kept by each background pixel. In other words, generates the historic for each pixel, indeed on certain computer vision applications the background can be dynamic; several intensities were projected on the same pixel. This paper describe a novel approach which integrate both Singular Value Decomposition (SVD) of each image to increase the compactness density distribution and hybrid color space suitable to this case constituted by the three relevant chromatics levels deduced by histogram analysis. In fact the proposed technique presents the efficiency of SVD and color information to subtract background pixels corresponding to shadows pixels. This method has been applied on colour images issued from soccer video. In the other hand to achieve some statistics information about players ongoing of the match (football, handball, volley ball, Rugby...) as well as to refine their strategy coach and leaders need to have a maximum of technical-tactics information. For this reason it is prominent to elaborate an algorithm detecting automatically interests color regions (players) and solve the confusion problem between background and foreground every moment from images sequence.

Fusion of color, local spatial and global frequency information for face recognition

This paper presents a novel face recognition method by means of fusing color, local spatial and global frequency information. Specifically, the proposed method fuses the multiple features derived from a hybrid color space, the Gabor image representation, the local binary patterns (LBP), and the discrete cosine transform (DCT) of the input image. The novelty of this paper is threefold. First, a hybrid color space, the RC r Q color space, is constructed by combining the R component image of the RGB color space and the chromatic component images, C r and Q, of the YC b C r and YIQ color spaces, respectively. The RC r Q hybrid color space, whose component images possess complementary characteristics, enhances the discriminating power for face recognition. Second, three effective image encoding methods are proposed for the component images in the RC r Q hybrid color space to extract features: (i) a patch-based Gabor image representation for the R component image, (ii) a multi-resolution LBP feature fusion scheme for the C r component image, and (iii) a component-based DCT multiple face encoding for the Q component image. Finally, at the decision level, the similarity matrices generated using the three component images in the RC r Q hybrid color space are fused using a weighted sum rule. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show that the proposed method improves face recognition performance significantly. In particular, the proposed method achieves the face verification rate (ROC III curve) of 92.43%, at the false accept rate of 0.1%, compared to the FRGC baseline performance of 11.86% face verification rate at the same false accept rate.

Color space normalization: Enhancing the discriminating power of color spaces for face recognition

This paper presents the concept of color space normalization (CSN) and two CSN techniques, i.e., the within-color-component normalization technique (CSN-I) and the across-color-component normalization technique (CSN-II), for enhancing the discriminating power of color spaces for face recognition. Different color spaces usually display different discriminating power, and our experiments on a large scale face recognition grand challenge (FRGC) problem reveal that the RGB and XYZ color spaces are weaker than the I 1I 2I 3, YUV, YIQ, and LSLM color spaces for face recognition. We therefore apply our CSN techniques to normalize the weak color spaces, such as the RGB and the XYZ color spaces, the three hybrid color spaces XGB, YRB and ZRG, and 10 randomly generated color spaces. Experiments using the most challenging FRGC version 2 Experiment 4 with 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, show that the proposed CSN techniques can significantly and consistently improve the discriminating power of the weak color spaces. Specifically, the normalized RGB, XYZ, XGB, and ZRG color spaces are more effective than or as effective as the I 1I 2I 3, YUV, YIQ and LSLM color spaces for face recognition. The additional experiments using the AR database validate the generalization of the proposed CSN techniques. We finally explain why the CSN techniques can improve the recognition performance of color spaces from the color component correlation point of view.

Producing stylized videos using the AnimVideo rendering tool

AbstractStylized rendering is the process of generating images or videos that can have the visual appeal of pieces of art, expressing the visual and emotional characteristics of artistic styles. A major problem in stylizing videos is the absence of temporal coherence, something that results in flickering of the structural drawing elements (such as brush strokes or curves), also known as swimming. This article describes the AnimVideo rendering tool that was developed for stylizing videos with temporal coherence. The temporal coherence is achieved by first fully segmenting the input video with a fast fuzzy segmentation algorithm that uses hybrid color spaces and motion information. The result of the segmentation algorithm is used to constrain the result of an optical flow algorithm, given as dense optical flow maps that are then used to correctly move, remove, or add structural drawing elements. The combination of these two methods is referred to as constrained optical flow, and we also provide the option of initializing the optical flow computation with displacement maps computed by homographies that map objects in adjacent frames. Also, we briefly describe some stylized rendering methods that were implemented in the tool. Finally, experimental results are shown, including snapshots of the tool's interface and illustrative examples of the produced renderings that validates the proposed techniques. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 100–110, 2009.

A Hybrid Color and Frequency Features Method for Face Recognition

This correspondence presents a novel hybrid Color and Frequency Features (CFF) method for face recognition. The CFF method, which applies an Enhanced Fisher Model (EFM), extracts the complementary frequency features in a new hybrid color space for improving face recognition performance. The new color space, the RIQ color space, which combines the R component image of the RGB color space and the chromatic components I and Q of the YIQ color space, displays prominent capability for improving face recognition performance due to the complementary characteristics of its component images. The EFM then extracts the complementary features from the real part, the imaginary part, and the magnitude of the R image in the frequency domain. The complementary features are then fused by means of concatenation at the feature level to derive similarity scores for classification. The complementary feature extraction and feature level fusion procedure applies to the I and Q component images as well. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show that i) the hybrid color space improves face recognition performance significantly, and ii) the complementary color and frequency features further improve face recognition performance.

Color image segmentation by pixel classification in an adapted hybrid color space. Application to soccer image analysis

In this paper, we propose an original approach in order to improve the results of color image segmentation by pixel classification. We define a new kind of color space by selecting a set of color components which can belong to any of the different classical color spaces. Such spaces, which have neither psycho-visual nor physical color significance, are named hybrid color spaces. We propose to classify pixels represented in the hybrid color space which is specifically designed to yield the best discrimination between the pixel classes. This space, which is called the adapted hybrid color space, is built by means of a sequential supervised feature selection scheme. This procedure determines the adapted hybrid color space associated with a given family of images. Its dimension is not always equal to three, as for classical color spaces. The effectiveness of our color segmentation method is assessed in the framework of soccer image analysis. The team of each player is identified by the colors of its soccer suit. The aim of the segmentation procedure is to extract meaningful regions representing the players and to recognize their teams.