Fuzzy Color Research Articles

Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth

This paper presents a complete Fuzzy-Genetic-based self-calibrating illumination intensity-invariant colour classification system. Previously, we have developed a novel fuzzy colour processing technique called Fuzzy Colour Contrast Fusion (FCCF) that selectively and adaptively corrects colours depicting target colour objects. FCCF has been proven to compensate for the effects of spatially varying illumination intensities in the scene, in various colour spaces. However, FCCF requires a huge set of parameters that is extremely tedious to calibrate by hand. To address these problems, we present a system that combines FCCF with a Heuristic-Assisted Genetic Algorithm (HAGA). FCCF-HAGA fully automates the fine-tuning of all colour descriptors, with significantly improved colour classification accuracy. Furthermore, we have reduced FCCF’s storage space requirements by processing colour channels selectively at varying colour depths. This is accomplished by combining a Variable Colour Depth (VCD) algorithm with FCCF that searches for the most effective colour depth for each colour channel. Our results show that for all cases, the FCCF-HAGA-VCD combination improves pie-slice colour classification. For six different target colours, under varying illuminations, the hybrid algorithm was able to yield 17.63% higher overall colour classification accuracy as compared to the pure fuzzy approach. Furthermore, it was able to reduce LUT storage space requirements by 78.06%, as compared to the full-colour depth LUT.

Fuzzy color histogram-based video segmentation

We present a fuzzy color histogram-based shot-boundary detection algorithm specialized for content-based copy detection applications. The proposed method aims to detect both cuts and gradual transitions (fade, dissolve) effectively in videos where heavy transformations (such as cam-cording, insertions of patterns, strong re-encoding) occur. Along with the color histogram generated with the fuzzy linking method on L*a*b* color space, the system extracts a mask for still regions and the window of picture-in-picture transformation for each detected shot, which will be useful in a content-based copy detection system. Experimental results show that our method effectively detects shot boundaries and reduces false alarms as compared to the state-of-the-art shot-boundary detection algorithms.

Fuzzy Set Theoretical Analysis of Human Membership Values on the Color Triangle

The present study considers a fuzzy color system in which three membership functions are constructed on the RGB color triangle. This system can process a fuzzy input (as the membership values of subjects) to an RGB system and output the center of gravity of three weights associated with respective grades. Three membership functions are applied to the RGB color triangle relationship. By treating three membership functions of redness, greenness, and blueness on the RGB color triangle, an average color value can be easily obtained as the center of gravity of the fuzzy output. The differences between fuzzy input and inference output are described, and the relationship between the centers of gravity of fuzzy inputs and inference outputs for fuzzy inputs are shown in the present paper. A technique for obtaining expressions of the RGB color triangle using the fuzzy set theoretical method has been reported [5] and improved [6]. In the previous study, the relationship between input fuzzy sets with a plateau on the RGB triangle and fuzzy inputs of conical membership functions was examined. The RGB color triangle (plane) represents the hue and saturation of a color [8]. The six fundamental colors and white can be represented on the same color triangle (See Fig. 1). Vague colors on the RGB color triangle were clarified. In the present study, the membership value on the RGB triangular system are examined to determine the average color value as the center of gravity of the attribute information of vague colors. This fuzzy set theoretical approach is useful for vague color information processing, color-naming systems, and similar applications.

ENERGY AWARE FUZZY COLOR SEGMENTATION ALGORITHM — AN APPLICATION TO CRIMINAL IDENTIFICATION USING MOBILE DEVICES

Since its advent, the use of digital camera in mobile phones is getting more popular, where information retrieval based on visual appearance of an object is very useful when specific parameters for the object are not known. Though it is well-liked, it needs energy aware algorithms to carry out the various tasks such as segmentation and feature extraction. In this paper, a new energy aware fuzzy color segmentation algorithm is proposed and which has been applied for face segmentation in criminal identification using mobile devices. The criminals in the application are in three classes. They are New Criminal (NC), Suspected Criminal (SC) and Confirmed Criminal (CC). It is basically a mobile image-based content search engine that takes photographs of criminals as image queries and finds their relevant contents by matching them to the similar contents in the criminal databases. The energy aware fuzzy color segmentation is used to obtain the most significant parts of an image — facial regions of the persons and which are used in building image-based queries to the databases. Content search methodology in the application is also improved through the fuzzy modeling to make the application more flexible and simpler. Through the experiment conducted, it has been found that the proposed color segmentation algorithm is more robust and it reduces the computational time in searching process by minimizing the number of false cases. It could detect the faces in the images where the other known algorithms have failed to detect.

A new approach for unsupervised segmentation

Texture is an important spatial feature useful for identifying objects or regions of interest in an image. This paper presents two fuzzy logic-based approaches for colour textures. They are fuzzy texture unit and colour fuzzy texture spectrum. A method is proposed for segmentation using colour fuzzy texture descriptor for colour texture images. This paper discusses two steps. Firstly, the presence of colour texture is locally as well as globally represented. Secondly, with the proposed global descriptor, segmentation operation is performed by SOMs algorithm. Compared with moment features, our method achieves good segmentation results on colour texture images, demonstrating the performance of our proposal.

On Color Cluster Analysis with Three-dimensional Fuzzy Color Ball

The focus of this paper is on devising an efficient clustering task for arbitrary color data. In order to tackle this problem, the inherent uncertainty and vagueness of color are represented by a fuzzy color model. By taking a fuzzy approach to color representation, the proposed model makes a soft decision for the vague regions between neighboring colors. A definition on a three-dimensional fuzzy color ball is introduced, and the degree of membership of color is computed by employing a distance measure between a fuzzy color and color data. With the fuzzy color model, a novel fuzzy clustering algorithm for efficient partition of color data is developed.

Modified fuzzy ants clustering approach

Being trapped in local optima within clustering search space currently is nontrivial difficulty. In order to relieve such a difficulty, even using genetic algorithm to optimize the initial clusters for fuzzy c-means is still unsatisfied. Since genetic algorithm intensifies only the current best solution, it will easily gets trapped in local minima. The ant colony system, dissimilarly to genetic algorithm, recognizes that the solutions near the best solution are also good ones and they bring about smoothness of solution. This paper proposes a modified fuzzy ant clustering. Such a presented method is a combination of genetic algorithm, ant colony system and fuzzy c-means. It is employed in creating fuzzy color histogram in image retrieval application. The performance measurement relates to the percentages of accuracy of image retrieval. Experimental results show that the proposed approach yields the best results among others with respect to sensitivity and robustness on dealing with lighting intensity changes, quantization errors, also changes in number of images and in size of color space, even the certain-range variation of a particular parameter of clustering.

Detection algorithm for color dynamic images by multiple surveillance cameras under low luminance conditions based on fuzzy corresponding map

An objects detection algorithm for color dynamic images from two cameras is proposed for a surveillance system under low illumination. It provides automatic calculation of a fuzzy corresponding map and color similarity for lower luminance conditions, which detects little chromatic regions in CCD camera images under lower illumination and presents regions with a possibility of occlusion situation. Experimental detection results for two dynamic images from real surveillance cameras in a downtown area in Japan under low luminance conditions show that the proposed algorithm has 15% improved accuracy compared with the independent detection algorithm in the same false alarm rate, which occlusion regions are correctly presented. Moreover, implementability for severe surveillance situation is discussed. The proposed algorithm is being considered for use in a low cost surveillance system at a relatively poor security downtown (shopping mall) area in Japan.

A New Fuzzy Color Correlated Impulse Noise Reduction Method

A new impulse noise reduction method for color images is presented. Color images that are corrupted with impulse noise are generally filtered by applying a grayscale algorithm on each color component separately or using a vector-based approach where each pixel is considered as a single vector. The first approach causes artefacts especially on edge and texture pixels. Vector-based methods were successfully introduced to overcome this problem. Nevertheless, they tend to cluster the noise and to receive a lower noise reduction performance. In this paper, we discuss an alternative technique which gives a good noise reduction performance while much less artefacts are introduced. The main difference between the proposed method and other classical noise reduction methods is that the color information is taken into account to develop (1) a better impulse noise detection method and (2) a noise reduction method that filters only the corrupted pixels while preserving the color and the edge sharpness. Experimental results show that the proposed method provides a significant improvement on other existing filters.

Knowledge-based image retrieval system

Most of the retrieval systems concentrate much on low-level features such as color, texture, shape and position. The present system is mainly developed based on the visual descriptors of the image such as color, texture and shape descriptors, etc. along with the high-level semantic analysis of the image content through different processing modules in the proposed architecture. Similarity measures are proposed and the performance evaluation has been done. As an image browser, apart from retrieving images by image example, it also supports query by natural language. The present system works well both online and offline.We have used unsupervised Kohonen’s self-organizing maps (SOM) technique to train the images and our own indexing scheme with reference system based on R-tree SOM. We proposed an approach fuzzy color histogram for color retrieval, and Lie descriptors for the retrieval of shapes. We have also tested our appraoch for MPEG-7 images.

Fuzzy Set Theoretical Approach to the RGB Triangular System

The present study considers a fuzzy color system in which three membership functions are constructed on the RGB color triangle. This system can process a fuzzy input to an RGB system and output the center of gravity of three weights associated with respective grades. Three membership functions are applied to the RGB color triangle relationship. By treating three membership functions of redness, greenness, and blueness on the RGB color triangle, a target color can be easily obtained as the center of gravity of the output fuzzy set. In the present paper, the differences among fuzzy input, inference output, and chromaticity are described, and the relationship between inference outputs for crisp inputs and inference outputs for fuzzy inputs are shown on the chromaticity diagram.

Histogram-based fuzzy colour filter for image restoration

A new impulse noise reduction method for colour images, called histogram-based fuzzy colour filter (HFC), is presented in this paper. The HFC filter is particularly effective for reducing high-impulse noise in digital images while preserving edge sharpness. Colour images that are corrupted with noise are generally filtered by applying a greyscale algorithm on each colour component separately. This approach causes artefacts especially on edge or texture pixels. Vector-based filtering methods were successfully introduced to overcome this problem. In this paper, we discuss an alternative technique so that no artefacts are introduced. The main difference between the new proposed method and the classical vector-based methods is the usage of colour component differences for the detection of impulse noise and the preservation of the colour component differences. The construction of the HFC filter involves three steps: (1) the estimation of the original histogram of the colour component differences, (2) the construction of suitable fuzzy sets for representing the linguistic values of these differences and (3) the construction of fuzzy rules that determine the output. Extensive simulation results show that the proposed filter outperforms many well-known filters (including vector-based approaches).

Region-based fit of color homogeneity measures for fuzzy image segmentation

In this paper we introduce an approach to automatically select a homogeneity measure for color image segmentation, on the basis of the characteristics of the region to be segmented. In a previous work we presented a fuzzy color path-based image segmentation proposal where membership degrees were computed from the connectivity between pixels, based on the homogeneity degree of the path joining them. To measure homogeneity, we aggregate resemblances between consecutive pixels using t-norms. Since a great variety of homogeneity measures can be found, we need to automatically select a suitable t-norm for a given region. For this purpose we firstly approximate a value characterizing the region surrounding the seed, studying a set of fixed paths. Secondly, we establish a functional relationship between this value and the parameter of a Weber t-norm. Based on this functional relationship we obtain the value of t-norm's parameter, corresponding to the homogeneity measure to be used in the segmentation process. We show that our approach performs well in different types of regions.

Retrieving images in fuzzy object-relational databases using dominant color descriptors

In this paper a fuzzy approach for image retrieval on the basis of color features is presented. The proposal deals with vagueness in the color description and introduces the use of fuzzy database models to store and retrieve imprecise data. To face the color description, the concept of dominant fuzzy color is proposed, using linguistic labels for representing the color information in terms of hue, saturation and intensity. To deal with fuzzy data in our database model, we use a general approach which can support the manipulation of fuzzy objects in an object-relational database system. This allows the retrieval of images by performing flexible queries on the database.

Image retrieval system using R-tree self-organizing map

Most of the retrieval systems concentrate much on low-level features such as color, texture, shape, position, etc. The present system is mainly developed based on the visual descriptors of the image such as color, texture and shape descriptors. We have used unsupervised Kohonen’s self-organizing maps (SOM) technique to train the images and our own indexing scheme with reference system based on R-tree SOM. We proposed an approach, fuzzy color histogram, for color retrieval and Lie descriptors for the retrieval of shapes. We have performed experiments and tested the proposed approach with our own image database constructed from Corel photo gallery.

Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques

In this paper, a method is proposed for the segmentation of color images using a multiresolution-based signature subspace classifier (MSSC) with application to psoriasis images. The essential techniques consist of feature extraction and image segmentation (classification) methods. In this approach, the fuzzy texture spectrum and the two-dimensional fuzzy color histogram in the hue-saturation space are first adopted as the feature vector to locate homogeneous regions in the image. Then these regions are used to compute the signature matrices for the orthogonal subspace classifier to obtain a more accurate segmentation. To reduce the computational requirement, the MSSC has been developed. In the experiments, the method is quantitatively evaluated by using a similarity function and compared with the well-known LS-SVM method. The results show that the proposed algorithm can effectively segment psoriasis images. The proposed approach can also be applied to general color texture segmentation applications.

Vertex‐strength of fuzzy graphs

The fuzzy coloring of a fuzzy graph was defined by the authors in Eslahchi and Onagh (2004). In this paper we define the chromatic fuzzy sum and strength of fuzzy graph. Some properties of these concepts are studied. It is shown that there exists an upper (a lower) bound for the chromatic fuzzy sum of a fuzzy graph.

Learning‐based fuzzy colour prediction system for more effective apparel design

PurposeThis paper aims to design and develop a learning‐based fuzzy colour prediction system for providing more effective apparel design in computer‐aided design system.Design/methodology/approachIn this study, we propose using a fuzzy system integrated with preliminary knowledge of colour prediction for facilitating apparel design. The performance of the proposed system is evaluated in terms of its computational efficiency and robustness. In addition, the proposed system is evaluated by target group of customers.FindingsIt was found that the performance of the proposed system is better than the traditional approach.Research limitations/implicationsAlthough the proposed system has some limitations, the outcome of this study could be used to produce a future breakthrough in providing an intelligent computer‐aided design system for apparel product.Originality/valueUsing such an approach, an apparel designer could predict the favourite colours of garment for a target group of customers. The system uses preliminary knowledge about the customers' profiles and evaluations. Such fuzzy approach for colour prediction is established, which is not used in a traditional way in apparel design.

Fuzzy colour category map for the measurement of colour similarity and dissimilarity

Fuzzy colour category map for the measurement of colour similarity and dissimilarity

Image retrieval based on fuzzy color histogram processing

Content-based image retrieval (CBIR) is a collection of techniques for retrieving images on the basis of features, such as color, texture and shape. An efficient tool, which is widely used in CBIR, is that of color image histograms. The classic method of color histogram creation results in very large histograms with large variations between neighboring bins. Thus, small changes in the image might result in great changes in the histogram. Moreover, the fact that each color space consists of three components leads to 3-dimensional histograms. Manipulating and comparing 3D histograms is a complicated and computationally expensive procedure. The need, therefore, for reduction of the three dimensions to one could lead to efficient approaches. This procedure of projecting the 3D histogram onto one single-dimension histogram is called histogram linking. In this paper, a new fuzzy linking method of color histogram creation is proposed based on the L*a*b* color space and provides a histogram which contains only 10 bins. The histogram creation method in hand was assessed based on the performances achieved in retrieving similar images from a widely diverse image collection. The experimental results prove that the proposed method is less sensitive to various changes in the images (such as lighting variations, occlusions and noise) than other methods of histogram creation.