Color Distribution Of Image Research Articles

IJARCCE - Computer and Communication Engineering

Content Based Image Retrieval (CBIR) techniques appeared in 1990s. It uses low-level features like color, texture and shape to describe image content, and breaks through the limitation of traditional text query technique. This project we are proposes an image retrieval method based on color-feature and texture-feature similarity score. Many methods can be used to describe color feature. In this project we will use color moment method because it has the lowest feature vector dimensions and lower computational complexity. The HSV color space is describes a specific color by it's hue, saturation, and brightness values. For the similarity measurement the first order mean, the second standard deviation, and the third skewness color moments have been provided to be efficient and effective in representing color distribution of images. In this paper, the retrieval results from color feature and texture feature are analyzed, furthermore texture can be through as repeated patterns of pixel over a spatial domain. Since there is no mathematical definition for texture, many different methods are proposed for computing texture. Here we are used different distance based similarity measurement methods are proposed, before we are doing texture features apply ranklet transform can be calculated at different resolutions using Haar wavelet supports.

Color Feature Extraction in Content based Image Retrieval based on Quaternion Space

Interest in the digital images has increased a lot over the last few years, but the process of locating a desired image in such a large and diverse image collection becomes very difficult. Traditionally text in different languages is used for efficient retrieval of images; it has several drawbacks such as language constraint and subjectivity of human perception. Content-based image retrieval is a technique which uses visual contents such as color texture and shape to search images from large image databases according to user’s desire. Color is the most commonly used feature for content based image retrieval. In many applications color histogram is used to represent extracted color features. The important drawback of usual color histogram based method is that, it does not take image color distribution into consideration and inflexibly partition the color spaces into a fixed number of bins. In this paper we propose a moment-preserving technique based on binary quaternion space for feature extraction. It aims to extract color features according to the image color distribution that effectively reduces the distortion incurred in the feature extraction process. We also propose an efficient clustering based algorithm to compare similarity between two histograms. It is observed that minimizing the distortion incurred in the extraction process can improve the accuracy of retrieval. Our experimental results show that the proposed extraction methods can improve the average retrieval precision rate by a factor of 25% over that of a color histogram based feature extraction method (binning method). It is also observed that, this technique effectively reduces the average retrieval time.

Geometric discriminative features for aerial image retrieval in social media

The aerial image recognition is an important problem in multimedia information retrieval in social media. In this paper, we propose a new approach by integrating aerial image's local features into a discriminative one which reflects both the geometric property and the color distribution of aerial image. Firstly, each aerial image is segmented into several regions in terms of their color intensities. And region connected graph (RCG), the links between the spatial neighboring regions, is presented to encode the spatial context of aerial images. Secondly, we mine frequent structures in the RCGs corresponding to training aerial images collected from social media. And a set of refined structures are selected among the frequent ones towards being more discriminative and less redundant. Finally, given a new aerial image, its sub-RCGs corresponding to all the refined structures are extracted and quantized into a discriminative feature for aerial image recognition. The experimental results validate the proposed method by providing a more accurate recognition result of the aerial images on different datasets from different social medias.

Color-Difference Threshold for Printed Images

Five ISO 400 images were used as test images and a method of limits psychophysical experiment was designed to test color-difference threshold in printed images. The color appearance of each original image was modified by an exponential function for CIELAB lightness and chroma, an offset function for CIELAB hue at 20 steps for each attributes respectively. The modified images and their originals were paired to form the test image pairs. The mean color differences of image pairs, ranged from 0 to 4 CIELAB units, were calculated by CIELAB color-difference formula and nearly uniformly divided into 21 grades for each attributes. The test image pairs were assessed in a CPC-8n lighting booth. 12 normal color vision observers took part in the experiment. The experimental results showed that the mean color-differences threshold for lightness, chroma and hue attributes were 1.49, 1.53 and 0.78 CIELAB units showing the threshold for hue was apparently smaller than that of lightness and chroma, and the thresholds of different images were dependent on the image content or color distribution.

Low-level and high-level prior learning for visual saliency estimation

Visual saliency estimation is an important issue in multimedia modeling and computer vision, and constitutes a research field that has been studied for decades. Many approaches have been proposed to solve this problem. In this study, we consider the visual attention problem blue with respect to two aspects: low-level prior learning and high-level prior learning. On the one hand, inspired by the concept of chance of happening, the low-level priors, i.e., Color Statistics-based Priors (CSP) and Spatial Correlation-based Priors (SCP), are learned to describe the color distribution and contrast distribution in natural images. On the other hand, the high-level priors, i.e., the relative relationships between objects, are learned to describe the conditional priority between different objects in the images. In particular, we first learn the low-level priors that are statistically based on a large set of natural images. Then, the high-level priors are learned to construct a conditional probability matrix blue that reflects the relative relationship between different objects. Subsequently, a saliency model is presented by integrating the low-level priors, the high-level priors and the Center Bias Prior (CBP), in which the weights that correspond to the low-level priors and the high-level priors are learned based on the eye tracking data set. The experimental results demonstrate that our approach outperforms the existing techniques.

The Adjustment of Texture Mapping Based on Image-Entropy

Mass terrain in four fork tree index structure Xia, draws Shi needs for effective of texture map, viewpoint of moved and texture retrieved Shi scene dynamic update directly effect to displayed efficiency, used macro points block match micro-texture map of method, effective to reduced has data of frequency refresh; introduced image entropy reflect image color distribution of space features, to improve original terrain texture, to out a more meet actual of terrain draws method; experimental indicates that in mass terrain texture map process in the, Increases the level of authenticity and real-time terrain rendering

Selecting Semantically‐Resonant Colors for Data Visualization

AbstractWe introduce an algorithm for automatic selection of semantically‐resonant colors to represent data (e.g., using blue for data about “oceans”, or pink for “love”). Given a set of categorical values and a target color palette, our algorithm matches each data value with a unique color. Values are mapped to colors by collecting representative images, analyzing image color distributions to determine value‐color affinity scores, and choosing an optimal assignment. Our affinity score balances the probability of a color with how well it discriminates among data values. A controlled study shows that expert‐chosen semantically‐resonant colors improve speed on chart reading tasks compared to a standard palette, and that our algorithm selects colors that lead to similar gains. A second study verifies that our algorithm effectively selects colors across a variety of data categories.

Use of Color Feature Extraction Technique based on Color Distribution and Relevance Feedback for Content based Image Retrieval

In this paper we use the color feature extraction technique that considers the image color distribution. For the color feature extraction process we use the clustering, where fixed number of clusters and variable number of clusters are formed. The proposed technique preserve the image color distribution and reduces the distortion that occurred during the feature extraction process by using binary quaternion moment preserving (BQMP) technique. With this system we devised efficient relevance feedback to improve the efficiency of the content based image retrieval system. Our Experimental results shows there is an improvement in proposed system over the prior binning technique and ACE.

Adaptive K-means clustering for Color Image Segmentation

According to the indeterminate clustering number k and initial cluster center in K-means clustering algorithm while used in color image segmentation, a new segmentation approach by adaptive K-means clustering algorithm was proposed. The major improvements include: Firstly, adopt Octree color quantization algorithm to quantize the color image, shown with representative characteristics; then, apply a mean-equivalent method to determine initial cluster center, based on the color distribution of image; finally, cluster with K-means algorithm, and define the optimal clustering number adaptively through a new cluster validity index designed in this paper. Experimental results show that the proposed algorithm not only gives more accurate clustering number than the other algorithm, but also be effective, which greatly reduces the human intervention and has high practical value.

Color Image Quantization based on Bacteria Foraging Optimization

Bacterial Foraging Optimization (BFO) is optimization technique proposed by K. M. Passino in 2002 To tackle complex search problems of the real world, scientists have been drawing inspiration from nature and natural creatures for years. Bacterial Foraging Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. A Color images Quantization is necessary if the display on which a specific image is presented works with less colors than the original image. While a lot of color reduction techniques exist in the literature, they are mainly designed for image compression as they tend to alter image color structure and distribution, the researchers are always finding alternative strategies for color quantization so that they may be prepared to select the most appropriate technique for the color quantization. The objective of this research work, is to implement a new algorithm for Color Image Quantization based on Bacteria Foraging Optimization. To compare the designed algorithm with other swarm intelligence techniques and to validate the proposed work. The proposed algorithm is then applied to commonly used images including the phantom images. The conducted experiments indicate that proposed algorithm generally results in a significant improvement of image quality compared to other well-known approaches.

Halfway through the semantic gap: Prosemantic features for image retrieval

We present here, an image description approach based on prosemantic features. These features are obtained through a two-level feature extraction process. A first level of features, related to image structure and color distribution, is extracted from the images, and used as input to a bank of classifiers, each one trained to recognize a given category. Packing together the scores, the features that we call prosemantic are obtained, and used to index images in an image retrieval system where searches are performed using relevance feedback. Prosemantic features have been evaluated on a public domain dataset, and compared against two different sets of features. Our experiments show that the use of prosemantic features allows for a more successful and quick retrieval with respect to the other features considered.

Removing Artefacts From Color and Contrast Modifications

This work is concerned with the modification of the gray level or color distribution of digital images. A common drawback of classical methods aiming at such modifications is the revealing of artefacts or the attenuation of details and textures. In this work, we propose a generic filtering method enabling, given the original image and the radiometrically corrected one, to suppress artefacts while preserving details. The approach relies on the key observation that artefacts correspond to spatial irregularity of the so-called transportation map, defined as the difference between the original and the corrected image. The proposed method draws on the nonlocal Yaroslavsky filter to regularize the transportation map. The efficiency of the method is shown on various radiometric modifications: contrast equalization, midway histogram, color enhancement, and color transfer. A comparison with related approaches is also provided.

Adaptive Color Feature Extraction Based on Image Color Distributions

This paper proposes an adaptive color feature extraction scheme by considering the color distribution of an image. Based on the binary quaternion-moment-preserving (BQMP) thresholding technique, the proposed extraction methods, fixed cardinality (FC) and variable cardinality (VC), are able to extract color features by preserving the color distribution of an image up to the third moment and to substantially reduce the distortion incurred in the extraction process. In addition to utilizing the earth mover's distance (EMD) as the distance measure of our color features, we also devise an efficient and effective distance measure, comparing histograms by clustering (CHIC). Moreover, the efficient implementation of our extraction methods is explored. With slight modification of the BQMP algorithm, our extraction methods are equipped with the capability of exploiting the concurrent property of hardware implementation. The experimental results show that our hardware implementation can achieve approximately a second order of magnitude improvement over the software implementation. It is noted that minimizing the distortion incurred in the extraction process can enhance the accuracy of the subsequent various image applications, and we evaluate the meaningfulness of the new extraction methods by the application to content-based image retrieval (CBIR). Our experimental results show that the proposed extraction methods can enhance the average retrieval precision rate by a factor of 25% over that of a traditional color feature extraction method.

Organization of Concentric Double-Opponent Receptive Fields in the Primary Visual Cortex by Using Independent Component Analysis

Event Abstract Back to Event Organization of Concentric Double-Opponent Receptive Fields in the Primary Visual Cortex by Using Independent Component Analysis Yasunobu Igarashi1*, Takaaki Aoki2, Rui Kimura3 and Shin Ishii4 1 National Center for Neurology and Psychiatry, Japan 2 Nara Institute of Science and Technology, Japan 3 RIKEN Brain Science Institute, Japan 4 Kyoto University, Japan Previous experimental studies showed that neurons in the primary visual cort ex (V1) have four firing properties to represent visual information. Four fi ring properties are named luminance gabor, oriented double-opponent, concent ric single-opponent, and concentric double-opponent receptive fields. Previo us theoretical studies demonstrated that redundancy compressions like indepe ndent component analysis (ICA) and sparse coding can derive basic functions corresponding to luminance gabor and oriented double-opponent receptive fiel ds from natural images. On the other hand, emergences of basic functions cor responding to two concentric receptive fields via redundancy compression of natural images have not been reported. In this study, we propose a low pass filtering function of neurons in lateral geniculate nucleus (LGN) is importa nt to derive concentric basic functions in addition to redundancy compressio n. Neurons in LGN project their axons to neurons in V1. Therefore, it is pla usible that LGN contributes to form receptive fields of V1. We show that the concentric double-opponent basis function can be obtained by applying the l ow pass filtering and Fast ICA to natural images in a computer simulation. H owever, the concentric single-opponent basis function cannot be not obtained . This reason is thought that the concentric double-opponent basis function functionally includes the concentric single-opponent basis function from the view point of redundancy compression. We also clarify how shapes of basic f unctions depend on ones of learning images, and how color components of basi c functions depend on a color distribution of learning images. Conference: Neuroinformatics 2010 , Kobe, Japan, 30 Aug - 1 Sep, 2010. Presentation Type: Poster Presentation Topic: Computational Neuroscience Citation: Igarashi Y, Aoki T, Kimura R and Ishii S (2010). Organization of Concentric Double-Opponent Receptive Fields in the Primary Visual Cortex by Using Independent Component Analysis. Front. Neurosci. Conference Abstract: Neuroinformatics 2010 . doi: 10.3389/conf.fnins.2010.13.00055 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 Jun 2010; Published Online: 11 Jun 2010. * Correspondence: Yasunobu Igarashi, National Center for Neurology and Psychiatry, Tokyo, Japan, igalab1@j-adni.org Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Yasunobu Igarashi Takaaki Aoki Rui Kimura Shin Ishii Google Yasunobu Igarashi Takaaki Aoki Rui Kimura Shin Ishii Google Scholar Yasunobu Igarashi Takaaki Aoki Rui Kimura Shin Ishii PubMed Yasunobu Igarashi Takaaki Aoki Rui Kimura Shin Ishii Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Salient Region Detection by Modeling Distributions of Color and Orientation

We present a robust salient region detection framework based on the color and orientation distribution in images. The proposed framework consists of a color saliency framework and an orientation saliency framework. The color saliency framework detects salient regions based on the spatial distribution of the component colors in the image space and their remoteness in the color space. The dominant hues in the image are used to initialize an expectation-maximization (EM) algorithm to fit a Gaussian mixture model in the hue-saturation (H-S) space. The mixture of Gaussians framework in H-S space is used to compute the inter-cluster distance in the H-S domain as well as the relative spread among the corresponding colors in the spatial domain. Orientation saliency framework detects salient regions in images based on the global and local behavior of different orientations in the image. The oriented spectral information from the Fourier transform of the local patches in the image is used to obtain the local orientation histogram of the image. Salient regions are further detected by identifying spatially confined orientations and with the local patches that possess high orientation entropy contrast. The final saliency map is selected as either color saliency map or orientation saliency map by automatically identifying which of the maps leads to the correct identification of the salient region. The experiments are carried out on a large image database annotated with ldquoground-truthrdquo salient regions, provided by Microsoft Research Asia, which enables us to conduct robust objective level comparisons with other salient region detection algorithms.

Effective Image Mining by Representing Color Histograms as Time Series

Due to the wide spread of digital libraries, digital cameras, and the increase access to WWW by individuals, the number of digital images that exist pose a great challenge. Easy access to such collections requires an index structure to facilitate random access to individual images and ease navigation of these images. As these images are not annotated or associated with descriptions, existing systems represent the images by their extracted low level features.In this paper, we demonstrate two image mining tasks, namely image classification and image clustering, which are preliminary steps in facilitating indexing and navigation. These tasks are based on the extraction of color distributions of images. Then, these color distributions are represented as time series. To make the representation more effective and efficient for the data mining tasks, we have chosen to represent the time series by a new representation called SAX (Symbolic Aggregate approXimation) [14]. SAX based representation is very effective because it reduces the dimensionality and lower bounds the distance measure. We demonstrate by our experiment the feasibility of our approach.

Fractal structure in the color distribution of natural images

The colorimetric organization of RGB color images is investigated through the computation of the correlation integral of their three-dimensional histogram. For natural color images, as a common behavior, the correlation integral is found to follow a power law, with a noninteger exponent characteristic of a given image. This behavior identifies a fractal or multiscale self-similar distribution of the colors contained in typical natural images. This finding of a possible fractal structure in the colorimetric organization of natural images complement other fractal properties previously observed in their spatial organization. Such fractal colorimetric properties may be helpful to the characterization and modeling of natural images, and may contribute to progress in vision.

Automatic Mood-Transferring between Color Images

This new color-conversion method offers users an intuitive, one-click interface for style conversion and a refined histogram-matching method that preserves spatial coherence.

Hierarchical multi-classifier system design based on evolutionary computation technique

Histogram feature representation is important in many classification applications for characterization of the statistical distribution of different pattern attributes, such as the color and edge orientation distribution in images. While the construction of these feature representations is simple, this very simplicity may compromise the classification accuracy in those cases where the original histogram does not provide adequate discriminative information for making a reliable classification. In view of this, we propose an optimization approach based on evolutionary computation (Back, Evolutionary algorithms in theory and practice, Oxford University Press, New York, 1996; Fogel, Evolutionary computation: toward a new philosophy of machine intelligence, 2nd edn. IEEE, Piscataway, NJ 1998) to identify a suitable transformation on the histogram feature representation, such that the resulting classification performance based on these features is maximally improved while the original simplicity of the representation is retained. To facilitate this optimization process, we propose a hierarchical classifier structure to demarcate the set of categories in such a way that the pair of category subsets with the highest level of dissimilarities is identified at each stage for partition. In this way, the evolutionary search process for the required transformation can be considerably simplified due to the reduced level of complexities in classification for two widely separated category subsets. The proposed approach is applied to two problems in multimedia data classification, namely the categorization of 3D computer graphics models and image classification in the JPEG compressed domain. Experimental results indicate that the evolutionary optimization approach, facilitated by the hierarchical classification process, is capable of significantly improving the classification performance for both applications based on the transformed histogram representations.

INTEGRATING GLOBAL AND LOCAL ANALYSIS OF COLOR, TEXTURE AND GEOMETRICAL INFORMATION FOR CATEGORIZING LARYNGEAL IMAGES

An approach to integrating the global and local kernel-based automated analysis of vocal fold images aiming to categorize laryngeal diseases is presented in this paper. The problem is treated as an image analysis and recognition task. A committee of support vector machines is employed for performing the categorization of vocal fold images into healthy, diffuse and nodular classes. Analysis of image color distribution, Gabor filtering, cooccurrence matrices, analysis of color edges, image segmentation into homogeneous regions from the image color, texture and geometry view point, analysis of the soft membership of the regions in the decision classes, the kernel principal components based feature extraction are the techniques employed for the global and local analysis of laryngeal images. Bearing in mind the high similarity of the decision classes, the correct classification rate of over 94% obtained when testing the system on 785 vocal fold images is rather encouraging.