Color Coherence Vectors Research Articles

A New Content-Based Image Retrieval System Using Deep Visual Features

Content-based image retrieval (CBIR) is a technique for images retrieval based on their visual features, i.e. induced by their pixels. The images are, classically, described by the image feature vectors. Those vectors reflect the texture, color or a combination of them. The accuracy of the CBIR system is highly influenced by the (i) definition of the image feature vector describing the image, (ii) indexing and (iii) retrieval process. In this paper, we propose a new CBIR system entitled ISE (Image Search Engine). Our ISE system defines the optimum combination of color and texture features as an image feature vector, including the Particle Swarm Optimization (PSO) algorithm and employing an Interactive Genetic Approach (GA) for the indexing process. The performance analysis shows that our suggested PCM (Proposed Combination Method) upgrades the average precision metric from 66.6% to 89.30% for the “Food” category color histogram, from 77.7% to 100% concerning CCVs (Color Coherence Vectors) for the “Flower” category and from 58% to 87.65% regarding the DCD (Dominant Color Descriptor) for the “Building” category using the Corel dataset. Besides, our ISE system showcases an average precision of 98.23%, which is significantly higher than other CBIR systems presented in related works.

Using Color Coherence Vectors to Evaluate the Performance of Hydrologic Data Assimilation

AbstractAn inadequate characterization of hydrogeological properties can significantly decrease the trustworthiness of subsurface flow and transport model predictions. A variety of data assimilation methods have been proposed in order to estimate hydrogeological parameters from spatially scarce data by incorporating them into the governing physical models. In order to quantify the accuracy of the estimations, several metrics have been used such as Rank Histograms, root‐mean‐square error (RMSE), and Ensemble Spread. However, these commonly used metrics do not regard the spatial correlation of the aquifer's properties. This can cause permeability fields with very different spatial structures to have similar histograms or RMSE. In this paper, we propose an approach based on color coherence vectors (CCV) for evaluating the performance of these estimation methods. CCV is a histogram‐based technique for comparing images that incorporate spatial information. We represent estimated fields as digital three‐channel images and use CCV to compare and quantify the accuracy of estimations. The appealing feature of this technique is that it considers the spatial structure embedded in the estimated fields. The sensitivity of CCV to spatial information makes it a suitable metric for assessing the performance of data assimilation techniques. Under various factors, such as numbers of measurements and structural parameters of the log conductivity field, we compare the performance of CCV with the RMSE.

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM.

The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts. The simple linear iterative clustering method, which is based on superpixel theory, is then used to generate a patch from the selected images. After manually labeling each patch, they are divided into two categories: wheat ears and background. The color feature “Color Coherence Vectors,” the texture feature “Gray Level Co-Occurrence Matrix,” and a special image feature “Edge Histogram Descriptor” are then exacted from these patches to generate a high-dimensional matrix called the “feature matrix.” Because each feature plays a different role in the classification process, a feature-weighting fusion based on kernel principal component analysis is used to redistribute the feature weights. Finally, a twin-support-vector-machine segmentation (TWSVM-Seg) model is trained to understand the differences between the two types of patches through the features, and the TWSVM-Seg model finally achieves the correct classification of each pixel from the testing sample and outputs the results in the form of binary image. This process thus segments the image. Next, we use a statistical function in Matlab to get the exact a precise number of ears. To verify these statistical numerical results, we compare them with field measurements of the wheat plots. The result of applying the proposed algorithm to ground-shooting image data sets correlates strongly (with a precision of 0.79–0.82) with the data obtained by manual counting. An average running time of 0.1 s is required to successfully extract the correct number of ears from the background, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on wheat seedlings.

An Approach to Explore the Role of Color Models and Color Descriptors in the Optimization of Semantic Gap in Content based Image Retrieval

based image retrieval (CBIR) systems retrieve images based on their primitive features such as color, texture, shape etc. The semantic gap is defined as the inconsistency between the image retrieval based on these low level image features and high level human semantics. In this paper, the comparative analysis of various color model transformations is presented with the help of our proposed methods based on three color descriptors i.e. color histogram, color moments and color coherence vectors to determine the applicability of these models and descriptors for the reduction of semantic gap. Support vector machines are used to classify images into different semantic classes. The results are inferred with the help of performance parameters like precision, recall, and mean average precision. Experimental results suggest that the proposed approach gives a good evaluation of the applicability of color models as well as color descriptors for optimization of semantic gap in CBIR.

A Novel Color Coherence Vector Based Obstacle Detection Algorithm for Textured Environments

 Abstract—This paper presents a novel obstacle detection algorithm that makes use of color information and color coherence vectors for robust obstacle detection. The algorithm makes use of color cue to classify a pixel in an image as an obstacle or a path. Color is one of the prominent image features. Color information is readily available as input from a color camera. Our algorithm makes use of coherence vectors for representation and matching instead of histograms. A color histogram provides no spatial information. It merely describes the color information present in an image. Color coherence vectors represent pixels as either coherent or incoherent. Color coherence vectors prevent coherent pixels from getting matched with incoherent pixels. The color histogram cannot make such fine distinction. The algorithm is tested with a challenging indoor and outdoor image set. Test results show that our algorithm performs better than available color based obstacle detection approaches.

Defining a new feature set for content‐based image analysis using histogram refinement

AbstractThe proposed method is based on color histogram. A new set of features are proposed for content‐based image retrieval (CBIR) in this article. The selection of the features is based on histogram analysis. Standard histograms, because of their efficiency and insensitivity to small changes, are widely used for CBIR. But the main disadvantage of histograms is that many images of different appearances can have similar histograms because histograms provide coarse characterization of an image. We define an algorithm that utilizes the concept of Histogram Refinement (Pass and Zabih, IEEE Workshop on Applications of Computer Vision (1996), 96–102) and we call it color refinement method. Color refinement method splits the pixels in a given bucket into several classes just like histogram refinement method. The classes are all related to colors and are based on color coherence vectors. After the calculation of clusters using color refinement method, inherent features of each of the cluster is calculated. These inherent features include size, mean, variance, major axis length, minor axis length, and angle between x‐axis and major axis of ellipse for various clusters. These inherent features are finally used for image retrieval using Euclidean distance. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 86–93, 2008

An effective method to detect and categorize digitized traditional Chinese paintings

Traditional Chinese painting (TCP) is the gem of Chinese traditional arts. More and more TCP images are digitized and exhibited on the Internet. Effectively browsing and retrieving them is an important problem that needs to be addressed. Gongbi (traditional Chinese realistic painting) and Xieyi (freehand style) are two basic types of traditional Chinese paintings. This paper proposes a scheme to detect TCPs from general images and categorize them into Gongbi and Xieyi schools. Low-level features such as color histogram, color coherence vectors, autocorrelation texture features and the newly proposed edge-size histogram are used to achieve the high-level classification. Support vector machine (SVM) is applied as the main classifier to obtain satisfactory classification results. Experimental results show the effectiveness of the method.

Image indexing and retrieval using signature trees

Significant research has focused on determining efficient methodologies for effective and speedy retrieval in large image databases. Towards that goal, the first contribution of this paper is an image abstraction technique, called variable-bin allocation (VBA), based on signature bitstrings and a corresponding similarity metric. The signature provides a compact representation of an image based on its color content and yields better retrieval effectiveness than when using classical global color histograms (GCHs) and comparable to the one obtained when using color-coherence vectors (CCVs). More importantly however, the use of VBA signatures allows savings of 75% and 87.5% in storage overhead when compared to GCHs and CCVs, respectively. The second contribution is the improvement upon an access structure, the S-tree, exploring the concept of logical and physical pages and a specialized nearest-neighbor type of algorithm, in order to improve retrieval speed. We compared the S-tree performance when indexing the VBA signatures against the SR-tree indexing GCHs and CCVs, since SR-trees are arguably the most efficient access method for high-dimensional points. Our experimental results, using a large number of images and varying several parameters, have shown that the combination VBA/S-tree outperforms the GCH/SR-tree combination in terms of effectiveness, access speed and size (up to 45%, 25% and 70% respectively). Due to the very high-dimensionality of the CCVs their indexing, even using an efficient access structure, the SR-tree, did not seem to be a feasible alternative.

Color-based image retrieval using spatial-chromatic histograms

Abstract The paper describes a new indexing methodology for image databases integrating color and spatial information for content-based image retrieval. This methodology, called Spatial-Chromatic Histogram (SCH), synthesizing in few values information about the location of pixels having the same color and their arrangement within the image, can be more satisfactory than standard techniques when the user would like to retrieve from the database the images that actually resemble the query image selected in their color distribution characteristics. Experimental trials on a database of about 3000 images are reported and compared with more standard techniques, like Color Coherence Vectors, on the basis of human perceptual judgments.