A Color and Composition-based Image Retrieval System by Image-Query Drawing Method with Color-Impression Database

A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. To address these concerns, this paper presents a new color histogram representation, called fuzzy color histogram (FCH), by considering the color similarity of each pixel's color associated to all the histogram bins through fuzzy-set membership function. A novel and fast approach for computing the membership values based on fuzzy c-means algorithm is introduced. The proposed FCH is further exploited in the application of image indexing and retrieval. Experimental results clearly show that FCH yields better retrieval results than CCH. Such computing methodology is fairly desirable for image retrieval over large image databases.

There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image Retrieval (CBIR) is certainly the only solution to this problem. Difference between colors of two images can be an important metric to measure their similarity or dissimilarity. Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric.In this study, COREL Database is used for an exhaustive study of various distance metrics on different color spaces. Euclidean distance, Manhattan distance, Histogram Intersection and Vector Cosine Angle distances are used to compare histograms in both RGB and HSV color spaces. So, a total of 8 distance metrics for comparison of images for the sake of CBIR are discussed in this work.

This paper in the research of Region-Based characteristic image retrieval method foundation, proposed a new kind of the color image retrieval method based on wavelet transformation G-Regions Of Interest (GROI). we first use HVS(Human Visual System) characteristic to choose the color space which fit for the visual characteristics, then use K-means clustering to extract the areas of interest in the wavelet transform domain, and using the local energy of the wavelet coefficients in the areas of interest as a texture feature, color's mean and variance as a color feature, the barycentric coordinates as a position feature, we calculate similarity of between the image content and retrieval. The simulation results show that this GROI method which combined color characteristic,texture characteristic and position characteristic can more accurately find the necessary content of the images to users, significantly improve the accuracy in color image retrieval.

A rapid growth in medical ultrasound database makes it difficult for medical practitioners to manage and search relevant data with good efficiency. Hence, a novel image retrieval technique using Mean Distance Local Binary Pattern (Mean Distance LBP) has been proposed for content-based image retrieval. The conventional local binary pattern (LBP) converts every pixel of image into a binary pattern based on their relationship with neighbourhood pixels. The proposed feature descriptor differs from local binary pattern as it transforms the mutual relationship of all neighbouring pixels in a binary pattern based on their standard deviation templates as well as Euclidean distance from the center pixel. Color feature and Gray Level Co-occurrence Matrix have also been used in this work. To prove the excellence of the proposed method, experiments have been conducted on two different databases of natural images and face images. Further, the method is applied on real time ultrasound database for retrieval of liver images from a set of ultrasound images of various organs. The performance has been observed using well-known evaluation measures, precision and recall, and compared with some state-of-art local patterns. Comparison shows a significant improvement in the proposed method over existing methods.

Research in content-based image retrieval today is a lively discipline, expanding in breadth. Discovering new and innovative processes of locating a desired image from an expanding collection of images has been a major area of interest for many professional fields. This has led to the increasing use of a technique called Content Based Image Retrieval which is used to retrieve images based on low-level features which are automatically driven such as color, shape and texture. CBIR draws many of its methods from the field of image processing and computer vision and can be regarded as subset of that field. Using this technique, the entire database may be searched to find the most closely matching image. In this paper, color and texture features of an image have been used to retrieve all the similar images from the image database. For this purpose, Block Color Histogram as color feature and Gray-level Co-occurrence Matrix (GLCM) and Gabor wavelets as texture features have been used. The proposed approach is very efficient since it uses the prominent and distinct features of an image useful for effective image retrieval using fuzzy heuristics according to human visual system.

In the field of computer vision and image processing CBIR (Content based image retrieval) system become an active research area. The CBIR technique involves feature extraction based on image features (like color, shape and texture) and defining rules for comparing image. A classifier is also used by authors for image classification. For this purpose an image database taken from James Z. Wang research group website is used by many authors. Due to enormous increase in the size of database traditional methods of image retrieval by using keywords does not provide accurate result. Thus the concept of CBIR system is emerged. In last couple of years many method of feature extraction is developed but since now researcher are not able to develop a system which has gained high accuracy of human visual perception in distinguishing pictures. Therefore earlier researchers have concentrated to increase the retrieval accuracy of CBIR system by using different combination of features. The objective of this paper is to present review on a CBIR technique which combined four kinds of features set including every kind of feature for developing feature vector. Color based features (histogram), statistical features (mean, median and standard deviation), spatial features (Gabor feature and wavelet features) and texture features (GLCM) is used for forming feature vector. A SVM classifier is used with CBIR system to increase retrieval accuracy. SVM (support vector machine) is a machine learning classifier which provides good classification accuracy. Keywords: CBIR, SVM, NN, GLCM, HSV Color space, Euclidean distance matrix.

Color Image Retrieval Using Compacted Feature Vector with Mean-Count Tree

In this project, we proposed a Content Based Image Retrieval (CB IR) system which is used to retrieve a relevant image from an outsized database. Textile images showed the way for the development of CB IR. It establishes the efficient combination of color, shape and texture features. Herethe textile image is given as dataset. The images in database are loaded. The resultant image is given as input to feature extraction technique which is transformation of input image into a setof features such as color, texture and shape. The texture feature of an image is taken out by using Gray levelco-occurrence matrix (GLCM). The color feature of an image is obtained by HSIcolor space. The shape feature of an imageis extorted by sobel technique. These algorithms are used to calculate the similarity between extracted features. These features are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification techniques such as Support Vector M achine (SVM) are used to classify the features of a query image by splitting the group such as color, shape and texture.Finally, the relevant images are retrievedfrom a large database and hence the efficiency of an image is plotted.The software used is MATLAB7.10 (matrix laboratory) which is built software applications

South Central Timor (TTS) is one of the districts that has a weaving culture and also produces woven cloth in East Nusa Tenggara. The many types of woven fabric from each TTS tribe makes outsiders and even native TTS people do not recognize the typical TTS woven fabric, therefore we need a system that can help facilitate the community in recognizing the type and motif of woven fabric. In this study, digital image processing is used to identify the type of woven fabric in the TTS district using the HSV color feature extraction method, and the GLCM texture feature, and to measure the similarity of woven fabric using the Euclidean distance method. The image data of the woven fabric used is the image of woven fabric from 3 tribes of TTS district, namely the Amanatun, Amanuban, and Mollo tribes. Identification of woven fabric motifs using the K-fold cross validation test with two stages, namely the training and testing stages. The results of testing variants using 10 fold get an accuracy rate for GLCM texture features of 55%, for HSV color features of 62.5% and a combination of color and texture features of 91.67%.

Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two different classifiers are used with color histograms in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces across different numbers of bins, and overall performance of each bin size is compared with that of other bin sizes. The research findings show that the performance of REVC system does not improve in terms of overall sensitivity rate, when the number of bins in color histogram is increased. Moreover, this paper identifies the advantage of using HSV color space over RGB in using color histogram for REVC systems. Furthermore, findings recognize the optimum number of bins in both RGB and HSV color spaces, and ANOVA (analysis of variance) is used to analyze experimental data, which identifies the optimum color histogram bin size used for HSV color space is significantly better than that used for RGB color space in REVC systems.

In this paper, a Kansei landscape image retrieval system named KIRCK is proposed, which is based on color feature and Kansei factors. Color feature is extracted in HSV color space and the similarity of color feature is estimated by color accumulation histogram intersection method. Multi-class Support Vector Machine is applied for the mapping between high-level Kansei labels and low-level image characteristics. After the multi-class SVM is trained, Kansei factors of images can be labeled automatically, and the similarity of images in Kansei space also can be estimated. Thus integrated retrieval results using color and Kansei factors can be obtained, and the experiment shows that these retrieval results are more satisfied than only using color feature or Kansei factors. Correlative feedback is also introduced to improve the performance of our color feature and Kansei factors image retrieval.

Color image retrieval using multispectral random field texture model and color content features

In content based image retrieval (CBIR) process, every image has been represented in a compact set of local visual features i.e. color, texture, and/or shape of images. This set of local visual features is known as feature vector. In the CBIR process, feature vectors of images have been used to represent or to identify similar images in adequate way. As a result, feature vector construction has always been considered as an important issue since it must reflect proper image semantics using minimal amount of data. The proposed CBIR scheme is based on the combination of color and texture features. In this work initially, we have converted the given RGB image into HSV color image. Subsequently, we have considered H (hue), S (saturation), and V (intensity) components for extraction of visual image features. The texture features have been extracted from the V component of the image using 2D dual-tree complex wavelet transform (2D DT-CWT) where it analyzes the textural patterns in six different directions i.e. ±15\(^{\circ }\), ±45\(^{\circ }\), and ±75\(^{\circ }\). At the same time, we have computed the probability histograms of H and S components of the image respectively and subsequently those are divided into non-uniform bins based on cumulative probability for extraction of color based features. So, in this work both the color and texture features have been extracted simultaneously. Finally, the obtained features have been concatenated to attain the final feature vector and same is considered in image retrieval process. We have tested the novelty and performance of the proposed work in two Corel, two objects, and, a texture image datasets. The experimental results reveal the acceptable retrieval performances for different types of datasets.

An image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the large database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and colour similarity. Retrieving images based on the contents which are colour, texture, and shape is called content-based image retrieval (CBIR). This paper discusses and describes about the colour features technique for image retrieval systems. Several colour features technique and algorithms produced by the previous researcher are used to calculate the similarity between extracted features. This paper also describes about the specific technique about the colour basis features and combined features (hybrid techniques) between colour and shape features.

Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database.Image contents are plays significant role for image retrieval.There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design.Each image has three contents such as: color, texture and shape features.Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results.Color histogram and texture features have potential to retrieve similar images on the basis of their properties.As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query.To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solution.