Color Quantization Research Articles

Evaluation of Image Quality Assessment Metrics: Color Quantization Noise

Evaluation of Image Quality Assessment Metrics: Color Quantization Noise

A New Approach to Locate the Gallbladder from Ultrasound Image Using Intensity Stretching and SOM-based Color Quantization

A New Approach to Locate the Gallbladder from Ultrasound Image Using Intensity Stretching and SOM-based Color Quantization

Analysis on Color Extraction of Daur Traditional Craftwork Based on Fuzzy Clustering Algorithm

In this paper, the disappearing daur culture and its color of traditional craftwork are surveyed and classified. By reviewing the relevant literature and video data, the RGB color module of single utensil and visual recognition image are selected to represent the pattern of this craftwork of daur. Specially, color quantization is achieved by feature extracting for the building of color database based on which a further color message classification is accomplished. Finally, fuzzy clustering is conducted to compute the complexity of color which also verifies its high precision.

Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features

This paper presents a new approach to index color images using the features extracted from the error diffusion block truncation coding (EDBTC). The EDBTC produces two color quantizers and a bitmap image, which are further processed using vector quantization (VQ) to generate the image feature descriptor. Herein two features are introduced, namely, color histogram feature (CHF) and bit pattern histogram feature (BHF), to measure the similarity between a query image and the target image in database. The CHF and BHF are computed from the VQ-indexed color quantizer and VQ-indexed bitmap image, respectively. The distance computed from CHF and BHF can be utilized to measure the similarity between two images. As documented in the experimental result, the proposed indexing method outperforms the former block truncation coding based image indexing and the other existing image retrieval schemes with natural and textural data sets. Thus, the proposed EDBTC is not only examined with good capability for image compression but also offers an effective way to index images for the content-based image retrieval system.

AN EFFECTIVE COLOR QUANTIZATION METHOD USING COLOR IMPORTANCE-BASED SELF-ORGANIZING MAPS

Color quantization is an important process for image processing and various applications. Up to now, many color quantization methods have been proposed. The self-organizing maps (SOM) method is one of the most effective color quantization methods, which gives excellent color quantization results. However, it is slow, so it is not suitable for real-time applications. In this paper, we present a color importance{based SOM color quantization method. The proposed method dynamically adjusts the learning rate and the radius of the neighborhood using color importance. This makes the proposed method faster than the conventional SOM-based color quantization method. We compare the proposed method to 10 well-known color quantization methods to evaluate performance. The methods are compared by measuring mean absolute error (MAE), mean square error (MSE), and processing time. The experimental results show that the proposed method is effective and excellent for color quantization. Not only does the proposed method provide the best results compared to the other methods, but it uses only 67.18% of the processing time of the conventional SOM method.

A mobile and web application-based recommendation system using color quantization and collaborative filtering

In this paper, a recommendation system based on a mobile and web application is proposed for indoor decoration. The main contribution of this work is to apply two-stage ltering using linear matching and collaborative ltering to make recommendations. In the mobile application part, the image of the medium captured by a mobile phone is analyzed using color quantization methods, and these color analysis results along with other user-dened parameters such as height, width, and type of the product are sent to the web server. In the web application part, a large data set is rst ltered via linear matching in which the color content of the medium and user-dened parameters received from the mobile application are matched to those for the products stored in the database. We then apply second-stage ltering, namely collaborative ltering, on the reduced data set. Performance evaluations of various color quantization methods and collaborative ltering methods used in the system are made. Results show the feasibility of using scalar quantization as a color quantization method and the K-nearest neighbor in the collaborative ltering method for our recommendation system. Overall evaluation of the system shows that our recommendation system provides around 90% accuracy.

Human perception‐based image segmentation using optimising of colour quantisation

This study presents an advanced histogram-based image segmentation method that enhances image segmentation quality, while greatly reducing the computational complexity. Unlike existing histogram-based methods, the authors optimise the size of bins in the colour histogram by using human perception-based colour quantisation and the clustering centroids are selected effectively without using a complex process. Additionally, an over-segmentation removal technique based on connected-component labelling is employed. This improves the segmentation quality by connectivity analysis. A comparison between the experimental results on the Berkeley Segmentation Dataset by the proposed method and the benchmark methods demonstrated that the proposed method enhanced the segmentation quality by improving the Probabilistic Rand Index and the Segmentation Covering values compared with those of the benchmark methods. The computation time using the proposed method is reduced by up to 91.63% compared with the computation time using benchmark methods.

Content-Based Image Retrieval using Color Quantization and Angle Representation

An efficient non-uniform color quantization and similarity measurement methods are proposed to enhance the content-based image retrieval (CBIR) applications. The HSV color space is selected because it is close to human visual perception system, and a non-uniform color method is proposed to quantize an image into 37 colors. The marker histogram (MH) vector of size 296 values is generated by segmenting the quantized image into 8 regions (multiplication of 45°) and count the occurrences of the quantized colors in their particular angles. To cope with rotated images, an incremental displacement to the MH is applied 7 times. To find similar images, we proposed a new similarity measurement and other 4 existing metrics. A uniform color quantization of related work is implemented too and compared to our quantization method. One-hundred test images are selected from the Corel-1000 images database. Our experimental results conclude high retrieving precision ratios compared to other techniques.

Content-based Image Retrieval using Image Partitioning with Color Histogram and Wavelet-based Color Histogram of the Image

paper presents two content-based image retrieval algorithms that are based on image partitioning. The retrieval in the first algorithm is based only on the image color feature represented by the color histogram, while the retrieval in the second one is based on the image color and texture features represented by the color histogram and Haar wavelet transform, respectively. In these algorithms, each image in the database and the query image are divided into 4-equal sized blocks. Color and texture features are extracted for each block. Distances between the blocks of the query image and the blocks of a database image are calculated, then, the similarity between the query image and the database image is calculated by finding the minimum cost matching based on most similar highest priority (MSHP) principle. A CBIR system that implements the proposed algorithms has been developed. To evaluate the effectiveness of the proposed algorithms, experiments have been carried out using different color quantization schemes for three different color spaces (HSV, YIQ and YCbCr) with two similarity measures, namely the Histogram Euclidean Distance and Histogram Intersection Distance. The WANG image database, which contains 1000 general-purpose color images, has been used in the experiments.

Quantum multidimensional color images similarity comparison

Based on the representation of quantum multidimensional color image, a complete scheme of solving the similarity of two quantum multidimensional color images is studied in depth. The two cases, the color image with segmentation information and without segmentation information, are considered, respectively. First, two images are connected together to form a new "link-state" according to the specific generation circuit. Then, through the Hadamard transformation on the link-state and repeated observations, the similarity value of the two quantum multidimensional color images is obtained. The simulation experiments, especially the two satellite images with a short interval and the two 3/4-color-alike color images with corresponding segmentation information, also show a wonderful performance, which drives the explorations on the multiple quantum color images processing.

A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm

Considering the difficulties in image segmentation caused by the complexity of diverse ecological environments and various artificial targets in high resolution remote sensing images, especially in city scene, and in order to overcome the limitations existing in the traditional segmentation algorithm, JSEG (J-Segmentation), for high resolution remote sensing image segmentation and to further improve the segmentation accuracy, WJSEG (Wavelet-JSEG), a novel multi-scale segmentation algorithm based on wavelet transform, is proposed, which is an improved JSEG algorithm. WJSEG is an improved form of JSEG in relation to three aspects, including color quantization, multi-scale segmentation and region merging by introducing the multi-scale analysis tool based on wavelet transform. Experiments have been conducted on high resolution SPOT 5 pan-sharpened multispectral image and IKONOS panchromatic image. These experimental results were compared with those gained by the traditional JSEG algorithm and the famous commercial software named eCognition, which validated the effectiveness and reliability of the proposed WJSEG algorithm.

Image quantization using improved artificial fish swarm algorithm

Most image compression algorithms suffer from several drawbacks: high-computational complexity, moderate reconstructed picture qualities, and a variable bit rate. In this paper, an efficient color image quantization technique that depends on an optimized Fuzzy C-means (OFCM) algorithm is proposed. It exploits the optimization capability of the improved artificial fish swarm algorithm to overcome the shortage of Fuzzy C-means algorithm. It uses error diffusion algorithms to obtain perceptually better images after quantization. Experiments are carried out to estimate the performance of the proposed OFCM algorithm in image compression using standard image set. The results indicate that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction. The performance efficiency of the proposed technique is compared with those of three other quantization algorithms. The Comparative results confirmed that the OFCM has potential in terms of both accuracy and perceptual quality as compared to recent methods of the literature.

Edge detection, color quantization, segmentation, texture removal, and noise reduction of color image using quaternion iterative filtering

Empirical mode decomposition (EMD) is a simple, local, adaptive, and efficient method for nonlinear and nonstationary signal analysis. However, for dealing with multidimensional signals, EMD and its variants such as bidimensional EMD (BEMD) and multidimensional EMD (MEMD) are very slow due to the needs of a large amount of envelope interpolations. Recently, a method called iterative filtering has been proposed. This filtering-based method is not as precise as EMD but its processing speed is very fast and can achieve comparable results as EMD does in many image and signal processing applications. We combine quaternion algebra and iterative filtering to achieve the edge detection, color quantization, segmentation, texture removal, and noise reduction task of color images. We can obtain similar results by using quaternion combined with EMD; however, as mentioned before, EMD is slow and cumbersome. Therefore, we propose to use quaternion iterative filtering as an alternative method for quaternion EMD (QEMD). The edge of color images can be detected by using intrinsic mode functions (IMFs) and the color quantization results can be obtained from residual image. The noise reduction algorithm of our method can be used to deal with Gaussian, salt-and-pepper, speckle noise, etc. The peak signal-to-noise ratio results are satisfactory and the processing speed is also very fast. Since textures in a color image are high-frequency components, we also can use quaternion iterative filtering to decompose a color image into many high- and low-frequency IMFs and remove textures by eliminating high-frequency IMFs.

A Hybrid Color Quantization Algorithm Incorporating a Human Visual Perception Model

Color quantization is a common image processing technique where full color images are to be displayed using a limited palette of colors. The choice of a good palette is crucial as it directly determines the quality of the resulting image. Standard quantization approaches aim to minimize the mean squared error (MSE) between the original and the quantized image, which does not correspond well to how humans perceive the image differences. In this article, we introduce a color quantization algorithm that hybridizes an optimization scheme based with an image quality metric that mimics the human visual system. Rather than minimizing the MSE, its objective is to maximize the image fidelity as evaluated by S‐CIELAB, an image quality metric that has been shown to work well for various image processing tasks. In particular, we employ a variant of simulated annealing with the objective function describing the S‐CIELAB image quality of the quantized image compared with its original. Experimental results based on a set of standard images demonstrate the superiority of our approach in terms of achieved image quality.

Automated Classification of Cricket Pitch Frames in Cricket Video

Automated detection of the cricket pitch is a fundamental step in content-based indexing and summarization of cricketvideos. In this paper, we propose visual-content based algorithms to automate the extraction of video frames with thecricket pitch in focus from input cricket videos. As a preprocessing step, we first select a subset of frames with a viewof the cricket field. This reduces the search space by eliminating frames that contain a view of the audience, close-upshots of specific players, advertisements, etc. The subset of frames containing the cricket field is then processed using astatistical modeling of the grayscale (brightness) histogram (SMoG). Since, in the present day, most videos are shot incolor and SMoG does not utilize this information, we propose an alternative: color quantization based region of interestextraction (CQRE). Experimental results demonstrate that successive application of the two methods outperforms eitherone applied exclusively, regardless of the quality of the input. The SMoG-CQRE combination for cricket pitch detectionyields an average accuracy of 98:6% in the best case (a high resolution video with good contrast) and an average accuracyof 87:9% in the worst case (a low resolution video with poor contrast). Since, the extraction of pitch frames only formsthe first step in analyzing key action frames in a match, we also present an an algorithm for player detection in theseframes.

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

spaces, color histograms, histogram distance measurements, size and quantization play an important role in retrieving images based on similarities. This paper presents a study of the effect of color quantization schemes for different color spaces (HSV, YIQ and YCbCr) on the performance of content-based image retrieval (CBIR), using different histogram distance measurements (Histogram Euclidean Distance and Histogram Intersection Distance). For the purpose of this study, a CBIR system that implements two content-based image retrieval algorithms has been developed. The first algorithm is based only on the color feature, while the second one is based on combination of the color and texture features. The color histogram is used for image color feature extraction and Haar wavelet transform is used for image texture feature extraction. The WANG image database, which contains 1000 general-purpose color images, has been used in the experiments of this study. The experimental results show which histogram distance measurement is best, which color space gives better retrieval precision, and the best quantization schemes for the considered color spaces, when using only the color feature, and when using a combination of the color and texture features.

Improved Image Retrieval with Color and Angle Representation

In this research, new ideas are proposed to enhance content-based image retrieval applications by representing colored images in terms of its colors and angles as a histogram describing the number of pixels with particular color located in specific angle, then similarity is measured between the two represented histograms. The color quantization technique is a crucial stage in the CBIR system process, we made comparisons between the uniform and the non-uniform color quantization techniques, and then according to our results we used the non- uniform technique which showed higher efficiency. In our tests we used the Corel-1000 images database in addition to a Matlab code, we compared our results with other approaches like Fuzzy Club, IRM, Geometric Histogram, Signature Based CBIR and Modified ERBIR, and our proposed technique showed high retrieving precision ratios compared to the other techniques.

Centroid Ratio for a Pairwise Random Swap Clustering Algorithm

Clustering algorithm and cluster validity are two highly correlated parts in cluster analysis. In this paper, a novel idea for cluster validity and a clustering algorithm based on the validity index are introduced. A Centroid Ratio is firstly introduced to compare two clustering results. This centroid ratio is then used in prototype-based clustering by introducing a Pairwise Random Swap clustering algorithm to avoid the local optimum problem of $k$ -means. The swap strategy in the algorithm alternates between simple perturbation to the solution and convergence toward the nearest optimum by $k$ -means. The centroid ratio is shown to be highly correlated to the mean square error (MSE) and other external indices. Moreover, it is fast and simple to calculate. An empirical study of several different datasets indicates that the proposed algorithm works more efficiently than Random Swap , Deterministic Random Swap , Repeated k-means or k-means++ . The algorithm is successfully applied to document clustering and color image quantization as well.

Saliency Tree: A Novel Saliency Detection Framework

This paper proposes a novel saliency detection framework termed as saliency tree. For effective saliency measurement, the original image is first simplified using adaptive color quantization and region segmentation to partition the image into a set of primitive regions. Then, three measures, i.e., global contrast, spatial sparsity, and object prior are integrated with regional similarities to generate the initial regional saliency for each primitive region. Next, a saliency-directed region merging approach with dynamic scale control scheme is proposed to generate the saliency tree, in which each leaf node represents a primitive region and each non-leaf node represents a non-primitive region generated during the region merging process. Finally, by exploiting a regional center-surround scheme based node selection criterion, a systematic saliency tree analysis including salient node selection, regional saliency adjustment and selection is performed to obtain final regional saliency measures and to derive the high-quality pixel-wise saliency map. Extensive experimental results on five datasets with pixel-wise ground truths demonstrate that the proposed saliency tree model consistently outperforms the state-of-the-art saliency models.

URINE COLOR AUTOMATIC IDENTIFICATION DEVICE BASED ON MICROCONTROLLER FRAMEWORK AND COLOR QUANTIZATION ALGORITHM

Urine color is an indicator of health status, especially for patients undergoing medical intervention treatment of urinary catheterization. Urinary tract infections are the most frequently developed infections among patients receiving treatment in medical institutions. Such infections can be detected from urine color, like in the case of the purple urine bag syndrome. However, it is a difficult task for non-nursing care staff and even the nursing staff to correctly conduct naked-eye identification without proper tools. To better assist both nursing and non-nursing care staff with the detection of infection signs in urine bag patients, a urine color automatic identification device has been developed. The device is based on microcontroller framework and color quantization algorithm. A hybrid color quantization algorithm and two features were proposed to identify the urine color. The identified color, as query data instead of human-described color keyword, can be used to retrieve the information from the database and then find possible symptoms for early warning. Instead of the nursing staff, the device can automatically identify the patient's urine color. From experimental results, the device with the proposed algorithm shows its capability and feasibility of the urine color automatic identification.