Dominant Color Descriptor Research Articles

비정형객체 검색을 위한 향상된 분할영역 색 기술자

 !/0%&#$1'2 3 '()4 5 6. 7 189:; %&?/0%&@A;BCC@A>D'()4EF:9'G D%&?M/%& #$ 6. #$ VW@AR# 6. :; VWaARb=>%&?/0VWaARb/0%&@AR#:`C%&1c-MPEG-7 Jd '()(dominant color descriptor)4EF:`ef'()4EF 6.  bghi :`jkl%!a 6m no@Apq:`Rb 1,973r<his7Dt7u4Rv:w@x,  bg ARR(Average Retrieval Ratio)4yz:`'G<MPEG-7 Jd '(?{:w6. AbstractThe paper presents a new way of visual descriptor for deformable object retrieval on the basis of partition based description. The proposed descriptor technology partitions a given object into boundary area and interior area and extracts a descriptor from each area. The final descriptor combines these descriptors. From a given image, deformable object is segmented. The center position of the deformable object is calculated. The object is partitioned into N × N blocks on the basis of the given center position. Blocks are classified as boundary area and interior area depending on the pixels in the block. The proposed descriptor consists of extracted MPEG-7 dominant descriptors from both the boundary and interior area. The performance of proposed method is tested on a database of 1,973 handbag images constructed with view point changes. ARR (Average Retrieval Rate) is used for the retrieval accuracy of the proposed algorithm, compared with MPEG-7 dominant color descriptor.Keyword : boundary descriptor, deformable object, object retrieval, color descriptor

Efficient image retrieval using advanced SURF and DCD on mobile platform

As the amount of digital image continues to grow in usage, users are experiencing increased difficulty in finding specific images in the image collection. This paper proposes a novel image searching scheme that extracts the image feature using combination of ASURF (Advanced Speed-Up Robust Feature) and DCD (Dominant Color Descriptor). The system for mobile image searches runs in real-time on iPhone, and can be easily used to find a natural color image. To evaluate the proposed scheme, we assessed the performance of simulation in term of average precision and F-score on two image database, which is commonly used in the field of image retrieval. The experimental results revealed that the proposed algorithm exhibited a significant improvement of over 14.4 % in retrieval effectiveness, compared to open source OpenSURF. The main contribution of this paper is that the proposed approach achieves high accuracy and stability by using ASURF and DCD in searching for natural image on mobile platform.

Accurate Approximation of the Earth Mover’s Distance in Linear Time

Color descriptors are one of the important features used in content-based image retrieval. The dominant color descriptor (DCD) represents a few perceptually dominant colors in an image through color quantization. For image retrieval based on DCD, the earth mover’s distance (EMD) and the optimal color composition distance were proposed to measure the dissimilarity between two images. Although providing good retrieval results, both methods are too time-consuming to be used in a large image database. To solve the problem, we propose a new distance function that calculates an approximate earth mover’s distance in linear time. To calculate the dissimilarity in linear time, the proposed approach employs the space-filling curve for multidimensional color space. To improve the accuracy, the proposed approach uses multiple curves and adjusts the color positions. As a result, our approach achieves order-of-magnitude time improvement but incurs small errors. We have performed extensive experiments to show the effectiveness and efficiency of the proposed approach. The results reveal that our approach achieves almost the same results with the EMD in linear time.

Saliency detection in computer rendered images based on object-level contrast

In this work, we propose a novel graphic saliency detection method to detect visually salient objects in images rendered from 3D geometry models. Different from existing graphic saliency detection methods, which estimate saliency based on pixel-level contrast, the proposed method detects salient objects by computing object-level contrast. Given a rendered image, the proposed method first extracts dominant colors from each object, and represents each object with a dominant color descriptor (DCD). Saliency of each object is then calculated by measuring the contrast between the DCD of the object and the DCDs of its surrounding objects. We also design a new iterative suppression operator to enhance the saliency result. Compared with existing graphic saliency detection methods, the proposed method can obtain much better performance in salient object detection. We further apply the proposed method to selective image rendering and achieve better performance over the relevant existing algorithm.

Colour and texture feature‐based image retrieval by using Hadamard matrix in discrete wavelet transform

Image retrieval is one of the most applicable image processing techniques, which has been used extensively. Feature extraction is one of the most important procedures used for interpretation and indexing images in content-based image retrieval systems. Effective storage, indexing and managing a large number of image collections is a critical challenge in computer systems. There are many proposed methods to overcome these problems. However, the rate of accurate image retrieval and speed of retrieval is still an interesting field of research. In this study, the authors propose a new method based on combination of Hadamard matrix and discrete wavelet transform (HDWT) in hue-min-max-difference colour space. An average normalised rank and combination of precision and recall are considered as metrics to evaluate and compare the proposed method against different methods. The obtained results show that the use of HDWT provides better performance in comparison with Haar discrete wavelet transform, colour layout descriptor, dominant colour descriptor and scalable colour descriptor, Padua point and histogram intersection.

A weighted dominant color descriptor for content-based image retrieval

Color has been extensively used in the process of image retrieval. The dominant color descriptor (DCD) that was proposed by MPEG-7 is a famous case in point. It is based on compactly describing the prominent colors of an image or a region. However, this technique suffers from some shortcomings; especially with respect to object-based image retrieval. In this paper, a new semantic feature extracted from dominant colors (weight for each DC) is proposed. The newly proposed technique helps reduce the effect of image background on image matching decision where an object’s colors receive much more focus. In addition, a modification to DC-based similarity measure is also proposed. Experimental results demonstrate that the proposed descriptor with the similarity measure modification performs better than the existing descriptor in content-based image retrieval application. The proposed descriptor considers as step forward to the object-based image retrieval.

Semantic Mapping of Color Feature and its Application in Content Based Image Retrieval

To support content based image retrieval, MPEG-7 is developed to define the content interfaces for images. In MPEG-7, Dominant Color Descriptor (DCD) is considered as the most important feature, and is widely used to describe the color features of an image. To support semantic queries from users, we proposed a color feature semantic mapping method in this work, which can translate the DCD values into semantic color names. The semantic mapping method is realized by constructing a mapping table between the DCD values and the semantic color names. To validate the effectiveness of our mapping method, an image retrieval experiment is conducted. From the comparison with the manually indexed description, the proposed mapping method is proved to be effective by the experiment results. Our work is very important to automatically generate the semantic description of an image and then support the users’ semantic retrieval queries.

Color Filter Based on Image ROI Extraction

With the rapid development of Internet, more and more enterprises establish business sites to achieve the purpose of online transactions. Taking taobao.com for example, hundreds of millions of goods trade on the trading platform. In front of the huge commodity image database, extraction of image features is very convenient for people to find out images of user requirement. This paper focus mainly on the color feature of images. Firstly, we segment ROI of images using grabCut algorithm; secondly, we extract primary color of images by using dominant color descriptor of MPEG 7; Thirdly, we adopt RGB color quantization to quantize the primary color. Finally achieve the purpose of image color navigation. I have done experiment to compare with some other methods, and find that the algorithms I adopted make a better performance.

Multi-Relation Image Retrieval and Annotation based on Holistic Approach &amp; Decision Tree

work of multi-relation image retrieval and annotation is based on the holistic approach and the decision tree. In this we have proposed that for the retrieval of similar images as that of query image Dominant color descriptor (DCD) is used, this descriptor uses the color feature for the retrieval of images. This creates the feature vector index. Test keywords are correlated with the feature vector index, the correlation is performed by multi class association to get the classes for processing on them. Classes which are not necessary are discarded using cross validation in the decision tree process Decision tree used to take the relevant classes and finally we calculate the Gain of feature vector and we get the retrieval of the images based on the query image with the associated keywords or annotation. This work has been implemented on MatLab 7.5 simulator.

Fast Multi-Operator Image Resizing and Evaluation

Current multi-operator image resizing methods succeeding in generating impressive results by using image similarity measure to guide the resizing process. An optimal operation path is found in the resizing space. However, their slow resizing speed caused by inefficient computation strategy of the bidirectional patch matching becomes a drawback in practical use. In this paper, we present a novel method to address this problem. By combining seam carving with scaling and cropping, our method can realize content-aware image resizing very fast. We define cost functions combing image energy and dominant color descriptor for all the operators to evaluate the damage to both local image content and global visual effect. Therefore our algorithm can automatically find an optimal sequence of operations to resize the image by using dynamic programming or greedy algorithm. We also extend our algorithm to indirect image resizing which can protect the aspect ratio of the dominant object in an image.

Compacted Dither Pattern Codes Over MPEG-7 Dominant Colour Descriptor In Video Visual Depiction

Reduction of feature space of visual descriptors has become important due to the ‘curse of dimensionality’ problem. This paper reports the efficiency and effectiveness of the Compacted Dither Pattern Code (CDPC) combined with the Bhattacharyya classifier over MPEG-7 Dominant Colour Descriptor (DCD). Both the CDPC and DCD syntactic features use a compact feature space for colour representation. The algorithmic comparison between the two is presented in this paper, and demonstrates that there are several competitive advantages of CDPC in feature extraction and classification stages when compared to MPEG-7 DCD. The embedded texel properties, spatial colour arrangements, high compactness, and robust feature representation of CDPC have proven its effectiveness in our experimental study. Visual description experiments were conducted for ten irregular shapes-based visual concepts in videos with three setups namely CDPC with Bhattacharyya classifier, DCD without spatial coherency and DCD with spatial coherency. The visual descriptions were performed with the TRECVID 2007 development key frame dataset. The experimental results are presented in terms of three common performance measures. The results show that CDPC with Bhattacharyya classifier provides a good generalised performance for irregular shapes-based visual description as compared to the other experimental setups.

Effectiveness of Dominant Color Descriptor Technique in Medical Image Retrieval Application

This paper presents a dominant color descriptor technique for medical image retrieval. The medical image system will collect and store into medical database. The purpose of dominant color descriptor (DCD) technique is to retrieve medical image and to display similar image using queried image. First, this technique will search and retrieve medical image based on keyword entered by user. After image is found, the system will assign this image as a queried image. DCD technique will calculate the image value of dominant color. Then, system will search and retrieve again medical image based on value of dominant color query image. Finally, the system will display similar images with the queried image to user. Simple application has been developed and tested using dominant color descriptor. Result based on experiment indicates this technique is effective and can be used for medical image retrieval. Keywords—Medical Image Retrieval, Dominant Color Descriptor.

Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization

Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD) PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF) technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.

Optimized image resizing using seam carving and scaling

We present a novel method for content-aware image resizing based on optimization of a well-defined image distance function, which preserves both the important regions and the global visual effect (the background or other decorative objects) of an image. The method operates by joint use of seam carving and image scaling. The principle behind our method is the use of a bidirectional similarity function of image Euclidean distance (IMED), while cooperating with a dominant color descriptor (DCD) similarity and seam energy variation. The function is suitable for the quantitative evaluation of the resizing result and the determination of the best seam carving number. Different from the previous simplex-mode approaches, our method takes the advantages of both discrete and continuous methods. The technique is useful in image resizing for both reduction/retargeting and enlarging. We also show that this approach can be extended to indirect image resizing.

Fixed Representative Colors Feature Extraction Algorithm for Moving Picture Experts Group-7 Dominant Color Descriptor

Problem statement: MPEG-7 has defined a set of color descriptors for the multimedia contents; one of these descriptors is the Dominant Color Descriptor (DCD). The DCD adopted the Generalized Lloyd Algorithm (GLA) as a feature extraction technique. However, using this algorithm in image retrieval systems may produce perceptually irrelevant images in the ranked results. Approach: In this study, we proposed a new technique for the extraction of the DCD called Fixed Representative Colors Feature Extraction Algorithm (FRCFE). This technique is based on converting the original image colors to a set of predefined co lors in the RGB color space. Also, we developed an image retrieval system to measure the effectives of this technique. Results: Experimental results revealed that FRCFE provides better accurate and perceptually relevant image retrieval for the MPEG- 7 DCD than GLA in the chosen dataset. Conclusion: Mapping the original image colors to a set of predefined colors preserved the main features of th e image and can produce more accurate results when used in image retrieval systems.

Effective image retrieval using dominant color descriptor and fuzzy support vector machine

Image retrieval is an active research area in image processing, pattern recognition, and computer vision. Relevance feedback has been widely accepted in the field of content-based image retrieval (CBIR) as a method to boost the retrieval performance. Recently, many researchers have employed support vector machines (SVMs) for relevance feedback. This paper presents a fuzzy support vector machine (FSVM) that is more robust to the four major problems encountered by the conventional SVMs: small size of samples, biased hyperplane, over-fitting, and real-time. To improve the performance, a dominant color descriptor (DCD) is also proposed. Experimental results based on a set of Corel images demonstrate that the proposed system performs much better than the previous methods. It achieves high accuracy and reduces the processing time greatly.

A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval

Dominant color descriptor (DCD) is one of the color descriptors proposed by MPEG-7 that has been extensively used for image retrieval. Among the color descriptors, DCD describes the salient color distributions in an image or a region of interest. DCD provides an effective, compact, and intuitive representation of colors presented in an image. In this paper, we will develop an efficient scheme for dominant color extraction. This approach significantly improves the efficiency of computation for dominant color extraction. In addition, we propose a modification for the MPEG-7 dissimilarity measure, which effectively improves the accuracy of perceptive similarity. Experimental results show that the proposed method achieves performance improvement not only in saving features extraction cost but also perceptually similar image retrieval.

Automatic video summarization based on MPEG-7 descriptions

The ever-growing amount of audiovisual content available has raised the need to develop systems allowing each of us to consume the information considered ‘essential’, adapted to our tastes, our preferences, our time and also to our capacities of receiving, consuming and storing that information. In other words, there is an increasing need to develop systems able to automatically summarize audiovisual information. This paper proposes a novel query-based summary creation mechanism using a relevance metric and a constraints schema implemented in the context of an automatic video summarization system based on MPEG-7 descriptions. In the context of the same system, this paper also proposes a human skin filter allowing to build summaries based on the presence or absence of human skin. This skin colour filter is based solely on the MPEG-7 Dominant Colour descriptor, which means that the content is skin filtered with a rather small amount of processing, without accessing and processing the video data.

Color and texture descriptors

This paper presents an overview of color and texture descriptors that have been approved for the Final Committee Draft of the MPEG-7 standard. The color and texture descriptors that are described in this paper have undergone extensive evaluation and development during the past two years. Evaluation criteria include effectiveness of the descriptors in similarity retrieval, as well as extraction, storage, and representation complexities. The color descriptors in the standard include a histogram descriptor that is coded using the Haar transform, a color structure histogram, a dominant color descriptor, and a color layout descriptor. The three texture descriptors include one that characterizes homogeneous texture regions and another that represents the local edge distribution. A compact descriptor that facilitates texture browsing is also defined. Each of the descriptors is explained in detail by their semantics, extraction and usage. The effectiveness is documented by experimental results.