Color Image Database Research Articles

Kernel Feature Extraction Approach for Color Image Recognition

Color Image Recognition is one of the most important fields in Pattern Recognition. Both Multi-set canonical correlation analysis and Kernel method are important techniques in the field of color image recognition. In this paper, we combine the two methods and propose one novel color image recognition approach: color image kernel canonical correlation analysis (CIKCCA). Color image kernel canonical correlation analysis is based on the theory of multi-set canonical correlation analysis and extracts canonical correlation features among the color image components. Then fuse the features of the color image components in the feature level, which are used for classification and recognition. Experimental results on the FRGC-v2 public color image databases demonstrate that the proposed approach acquire better recognition performance than other color recognition methods.

JPEG steganography detection with Benford's Law

In this paper we present a novel approach to the problem of steganography detection in JPEG images by applying a statistical attack. The method is based on the empirical Benford's Law and, more specifically, on its generalized form. We prove and extend the validity of the logarithmic rule in colour images and introduce a blind steganographic method which can flag a file as a suspicious stego-carrier. The proposed method achieves very high accuracy and speed and is based on the distributions of the first digits of the quantized Discrete Cosine Transform coefficients present in JPEGs. In order to validate and evaluate our algorithm, we developed steganographic tools which are able to analyse image files and we subsequently applied them on the popular Uncompressed Colour Image Database. Furthermore, we demonstrate that not only can our method detect steganography but, if certain criteria are met, it can also reveal which steganographic algorithm was used to embed data in a JPEG file.

Efficient Global and Region Content Based Image Retrieval

In this paper, we present an efficient content based image retrieval system that uses texture and color as visual features to describe the image and its segmented regions. Our contribution is of three directions. First, we use Gabor filters to extract texture features from the whole image or arbitrary shaped regions extracted from it after segmentation. Second, to speed up retrieval, the database images are segmented and the extracted regions are clustered according to their feature vectors using Self Organizing Map (SOM). This process is performed offline before query processing; therefore to answer a query, our system does not need to search the entire database images. Third, to further increase the retrieval accuracy of our system, we combine the region features with global features to obtain a more efficient system. The experimental evaluation of the system is based on a 1000 COREL color image database. From experimentation, it is evident that our system performs significantly better and faster compared with other existing systems. We provide a comparison between retrieval results based on features extracted from the whole image, and features extracted from image regions. The results demonstrate that a combination of global and region based approaches gives better retrieval results for almost all semantic classes.

Efficiency analysis of color image filtering

This article addresses under which conditions filtering can visibly improve the image quality. The key points are the following. First, we analyze filtering efficiency for 25 test images, from the color image database TID2008. This database allows assessing filter efficiency for images corrupted by different noise types for several levels of noise variance. Second, the limit of filtering efficiency is determined for independent and identically distributed (i.i.d.) additive noise and compared to the output mean square error of state-of-the-art filters. Third, component-wise and vector denoising is studied, where the latter approach is demonstrated to be more efficient. Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible. We also demonstrate that it is possible to roughly estimate whether or not the visual quality can clearly be improved by filtering.

An Image-Based Diagnostic Expert System for Corn Diseases

The annual worldwide yield losses due to pests are estimated to be billions of dollars. Integrated pest management (IPM) is one of the most important components of crop production in most agricultural areas of the world, and the effectiveness of crop protection depends on accurate and timely diagnosis of phytosanitary problems. Accurately identifying and treatment depends on the method which used in disease and insect pests diagnosis. Identifying plant diseases is usually difficult and requires a plant pathologist or well-trained technician to accurately describe the case. Moreover, quite a few diseases have similar symptoms making it difficult for non-experts to distinguish disease correctly. Another method of diagnosis depends on comparison of the concerned case with similar ones through one image or more of the symptoms and helps enormously in overcoming difficulties of non-experts. The old adage ‘a picture is worth a thousand words’ is crucially relevant. Considering the user's capability to deal and interact with the expert system easily and clearly, a web-based diagnostic expert-system shell based on production rules (i.e., IF < effects > THEN < causes >) and frames with a color image database was developed and applied to corn disease diagnosis as a case study. The expert-system shell was made on a 32-bit multimedia desktop microcomputer. The knowledge base had frames, production rules and synonym words as the result of interview and arrangement. It was desired that 80% of total frames used visual color image data to explain the meaning of observations and conclusions. Visual color image displays with the phrases of questions and answers from the expert system, enables users to identify any disease, makes the right decision, and chooses the right treatment. This may increase their level of understanding of corn disease diagnosis. The expert system can be applied to diagnosis of other plant pests or diseases by easy changes to the knowledge base.

Content-based retrieval of logo and trademarks in unconstrained color image databases using Color Edge Gradient Co-occurrence Histograms

In this paper, we present an algorithm that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme on compound color objects, for the retrieval of logos and trademarks in unconstrained color image databases. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, as compared to the simple color pixel difference classification of edges seen with the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Color Edge Gradient Co-occurrence Histogram (CEGCH). We also introduce a color quantization method based in the hue–saturation–value (HSV) color space, illustrating that it is a more suitable scheme of quantization for image retrieval, compared to the color quantization scheme introduced with the CECH. Experimental results demonstrate that the CEGCH and the HSV color quantization scheme is insensitive to scaling, rotation, and partial deformations, and outperforms the use of the CECH in image retrieval, with higher precision and recall. We also perform experiments on a closely related algorithm based on the Color Co-occurrence Histogram (CCH) and demonstrate that our algorithm is also superior in comparison, with higher precision and recall.

Color edge detection in natural scenes

Event Abstract Back to Event Color edge detection in natural scenes Thorsten Hansen1* and Karl R. Gegenfurtner1 1 Justus Liebig University, Department of General Psychology, Germany In a statistical analysis of over 700 natural scenes from the McGill calibrated color image database (Olmos and Kingdom, 2004, http://tabby.vision.mcgill.ca) we found that luminance and chromatic edges are statistically independent. These results show that chromatic edge contrast is an independent source of information that natural or artificial vision systems can linearly combine with other cues for the proper segmentation of objects (Hansen and Gegenfurtner, 2009, Visual Neuroscience).Here we investigate the contribution of color and luminance information to predict human-labeled edges. Edges were detected in three planes of the DKL color space (Lum, L-M, S-(L+M)) and compared to human-labeled edges from the Berkeley segmentation data set. We used a ROC framework for a threshold-independent comparison of edge detector responses (provided by the Sobel operator) to ground truth (given by the human marked edges). The average improvement as quantified by the difference between the areas under the ROC curves for pure luminance and luminance/chromatic edges was small. The improvement was only 2.7% if both L-M and S-(L+M) edges were used in addition to the luminance edges, 2.1% for simulated dichromats lacking an L-M channel, and 2.2% for simulated dichromats lacking an S-(L+M) channel. Interesting, the same improvement for chromatic information (2.5%) occurred if the ROC analysis was based on human-labeled edges in gray-scale images. Probably, observers use high-level knowledge to correctly mark edges even in the absence of a luminance contrast. While the average advantage of the additional chromatic channels was small, for some images a considerably higher improvement of up to 11% occurred. For few images the performance decreased. Overall, color was advantageous in 74% of the 100 images we evaluated. We interpret our results such that color information is on average beneficial for the detection of edges and can be highly useful and even crucial in special scenes. Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009. Presentation Type: Oral Presentation Topic: Sensory processing Citation: Hansen T and Gegenfurtner KR (2009). Color edge detection in natural scenes. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.141 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: 28 Aug 2009; Published Online: 28 Aug 2009. * Correspondence: Thorsten Hansen, Justus Liebig University, Department of General Psychology, Giessen, Germany, thorsten.hansen@psychol.uni-giessen.de 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 Thorsten Hansen Karl R Gegenfurtner Google Thorsten Hansen Karl R Gegenfurtner Google Scholar Thorsten Hansen Karl R Gegenfurtner PubMed Thorsten Hansen Karl R Gegenfurtner 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.

Discrete data clustering using finite mixture models

Finite mixture models have been applied for different computer vision, image processing and pattern recognition tasks. The majority of the work done concerning finite mixture models has focused on mixtures for continuous data. However, many applications involve and generate discrete data for which discrete mixtures are better suited. In this paper, we investigate the problem of discrete data modeling using finite mixture models. We propose a novel, well motivated mixture that we call the multinomial generalized Dirichlet mixture. The novel model is compared with other discrete mixtures. We designed experiments involving spatial color image databases modeling and summarization, and text classification to show the robustness, flexibility and merits of our approach.

Clustering of Count Data Using Generalized Dirichlet Multinomial Distributions

In this paper, we examine the problem of count data clustering. We analyze this problem using finite mixtures of distributions. The multinomial distribution and the multinomial Dirichlet distribution (MDD) are widely accepted to model count data. We show that these two distributions cannot be the best choice in all the applications, and we propose another model called the multinomial generalized Dirichlet distribution (MGDD) that is the composition of the generalized Dirichlet distribution and the multinomial, in the same way that the MDD is the composition of the Dirichlet and the multinomial. The estimation of the parameters and the determination of the number of components in our model are based on the deterministic annealing expectation-maximization (DAEM) approach and the minimum description length (MDL) criterion, respectively. We compare our method to standard approaches such as multinomial and multinomial Dirichlet mixtures to show its merits. The comparison involves different applications such as spatial color image databases indexing, handwritten digit recognition, and text document clustering.

Color Image Compression with Modified Fractal Coding on Spiral Architecture

The proposed approach (CICMFCSA), firstly, compose the one-plane image using the pixel’s trichromatic coefficients. One-plane image in traditional square structure is represented in Spiral Architecture for compression. On this Spiral Architecture image, proposed modified Fractal grey level image coding algorithm (MFCSA) is applied to get encoded image. In this modified Fractal coding, the numbers of domain blocks are optimized from 343 to 10 using local search. Extensive experiments are carried out on UCID - An Uncompressed Color Image Database. The proposed approach minimizes the encoding process time because of optimized domain blocks and one dimensional structure of Spiral Architecture and falls in the lossy compression category. The results of SFC and our approach are compared with respect to the time.

Cellular Neural Networks With Virtual Template Expansion for Retinal Vessel Segmentation

A retinal vessel segmentation method based on cellular neural networks (CNNs) is proposed. The CNN design is characterized by a virtual template expansion obtained through a multistep operation. It is based on linear space-invariant 3times3 templates and can be realized using existing chip prototypes like the ACE16K. The proposed design is capable of performing vessel segmentation within a short computation time. It was tested on a publicly available database of color images of the retina, using receiver operating characteristic curves. The simulation results show good performance comparable with that of the best existing methods

Image retrieval based on color distribution entropy

Color histogram is an important technique for color image database indexing and retrieving. However, the main problem with color histogram indexing is that it does not take the color spatial distribution into consideration. Previous researches have proved that the effectiveness of image retrieval increases when spatial feature of colors is included in image retrieval. In this paper, two new descriptors, color distribution entropy (CDE) and improved CDE (I-CDE), which introduce entropy to describe the spatial information of colors, are presented. In comparison with the spatial chromatic histogram (SCH) and geostat which also measure the global spatial relationship of colors, the experiment results show that CDE and I-CDE give better performance than SCH and geostat.

GENERAL IMAGE CLASSIFICATION USING ADAPTIVE CELLULAR COLOR DECOMPOSITION

In this paper, a coarse-to-fine hierarchical classification method based on the features derived from adaptive cellular color decomposition is proposed. The proposed method is general and can be applied to all kinds of color image databases as long as a sample set of images have been classified. In addition, the number of classes can be as versatile as required. To achieve the goal mentioned above, our method consists of two phases: color quantization and classification. In the color quantization step, cellular decomposition is used to adaptively quantize color images in the HSV color space since H and S components construct a hexagon structure that is same as the cellular pattern. In the classification step, a coarse-to-fine strategy is employed. In the coarse stage, five image-based features extracted directly from the quantization results of the query images are used to prune irrelevant database images. In the fine stage, two cluster-based features are extracted from a small set of candidate images using closest-cluster matching. On the other hand, according to feature evaluation, one image-based and two cluster-based features are selected to derive individual-based similarity measure, which, in turn, is used to measure image-to-image similarity. In addition, class-based similarity measure using class characteristics is proposed to evaluate image-to-class similarity. Candidate images are then sorted according to the similarity measure, which is a combination of individual-based and class-based similarity measures. Finally, k-NN rule is used to assign the query image to a single class according to the sorting results. The effectiveness and practicability of the proposed method have been demonstrated by various experimental results.

Image classification using color, texture and regions

A new classification method using color, texture and regions is proposed in this study. Image-based features related to color and local edge patterns are used to prune irrelevant database images for each query image. The proposed region matching is then applied to find the match to the query image from among the set of candidate images in the database. The dissimilarity of each pair of images can be calculated on the basis of the matching results. Finally, all the database images in the candidate set can be sorted by ascending dissimilarity values. To achieve the classification goal, the k-NN rule is used to assign a class label to the query image. Note that the main contribution of this paper is to select proper features for representing color, texture and region, which, in turn, are used to achieve effective classification results. More important, all features used in the proposed method, no matter color or texture, are presented in the simple form of histogram, yet leading to effective results. Even in the stage of region matching, color and texture features in histograms are also used to obtain homogeneous regions and to measure dissimilarity. In addition, the proposed classification method can be applied to all kinds of color image databases rather than specific databases. The number of classes can be as versatile as required by the application. The effectiveness and practicability of the proposed method has been demonstrated by various experiments.

Indexing chromatic and achromatic patterns for content-based colour image retrieval

In this paper, we present a method to represent achromatic and chromatic image signals independently for content-based image indexing and retrieval for image database applications. Starting from an opponent colour representation, human colour vision theories and modern digital signal processing technologies are applied to develop a compact and computationally efficient visual appearance model for coloured image patterns. We use the model to compute the statistics of achromatic and chromatic spatial patterns of colour images for indexing and content-based retrieval. Two types of colour images databases, one colour texture database and another photography colour image database are used to evaluate the performance of the developed method in content-based image indexing and retrieval. Experimental results are presented to show that the new method is superior or competitive to state-of-the-art content-based image indexing and retrieval techniques.

AN INTEGRATED APPROACH TO SHAPE AND COLOR-BASED IMAGE RETRIEVAL OF ROTATED OBJECTS USING HIDDEN MARKOV MODELS

An integrated approach to shape and color-based image retrieval, where the cues color and shape are both utilized in a local rather than a global way, is presented in this paper. An experimental retrieval system has been developed, which enables the user to search a color image database intuitively by presenting simple sketches. In order to be able to perform an elastic matching, which is especially needed in sketch-based image retrieval, objects in the images are represented by Hidden Markov Models. The use of streams (sets of features that are assumed to be statistically independent) within the HMM framework allows the integration of shape and color derived features into a single model, thereby allowing to control the influence of the different streams via stream weights. The approach has been evaluated on a color image database containing 120 different isolated objects with arbitrary orientation and showed good retrieval results with several users. Furthermore, the use of HMMs allows efficient pruning and thus a fast retrieval even with large databases.

VISUAL QUERYING BY COLOR PERCEPTIVE REGIONS

A major research subject in image databases is to support efficient and effective access to images based on their visual content. In color image databases, this requires to support image retrieval by both global and local chromatic features. Image retrieval by color regions is complicated by the fact that regions which are requested in the query should correspond to relevant colored regions in the retrieved images. In the following we present the PICASSO system, which supports image indexing and retrieval based on colors. The system exploits a pyramidal representation of images. Multiple descriptions of image properties are created at different levels of resolution thus allowing effective retrieval through specific queries as well as imprecise ones.

Color image retrieval based on hidden Markov models

In this correspondence, a new approach to retrieving images from a color image database is proposed. Each image in the database is represented by a two-dimensional pseudo-hidden Markov model (2-D PHMM), which characterizes the chromatic and spatial information about the image. In addition, a flexible pictorial querying method is used, by which users can paint the rough content of the desired images in a query picture. Image matching is achieved by comparing the query picture with each 2-D PHMM in the database. Experimental results show that the proposed approach is indeed effective.

Extracting 3-D information using 2-D images of seeds

At the National Seed Storage Laboratory, a color image database is being developed to characterize the phenotypic variations of genetic resources, specifically seeds. In order to collect seed characteristics such as size, shape, color, surface pattern, etc., a 3-D image measurement technique was developed using 2-D images. This information is needed to visualize all aspects of the seed characteristics in order to develop a germplasm inventory. Image processing techniques were developed to extract the key seed features. With this information, seed scientists will possess a tool capable of providing more precise data on seed genetic variability.