Color Coherence Vector Research Articles

A temporal correlation coefficient model of grain temperature field cloud maps in horizontal warehouses

The temperature field cloud map (TFCM) is a color temperature contour map that can represent grain temperature distribution of bulk grain in a lagre storage facility (grain warehouse). The analysis of temporal variation characteristics of temperature field cloud maps can assist to analyze the changes of grain temperatures to monitor grain conditions changes. Wherein the temporal correlation coefficient is a type of variation characteristic of temperature field cloud maps at time domain. This research established a temporal correlation coefficients model of grain temperature for different grain warehouses storing paddy, wheat and maize in different provinces of China. That features of TFCM were extracted include the color coherence vector (CCV), LBP feature vector (LBP), gray level co-occurrence matrix vector (GLCMV), RGB feature vector (RGBV) and LAB feature vector (LABV), which were combined successively as the model input. The RMSEs between the correlation coefficients calculated by the model and that calculated by the measured grain temperatures in these planes were analyzed. The results show that the RMSEs of models with different vector combinations as input were different, and the RMSEs were all less than 0.001. Therefore, the five feature combinations could be used as the model input. Afterwards, distribution of temporal correlation coefficients of temperature field cloud maps from different warehouses were analyzed. The results show that in the XOY, XOZ and YOX planes of grain warehouses, temporal correlation coefficients (in the range (0.98, 1)) proportion of temperature field cloud maps were more than 95%, 85% and 82%, respectively.

Garment Image Retrieval based on Grab Cut Auto Segmentation and Dominate Color Method

Abstract Targeted at the harmful effects of garment image retrieval at present, a new approach of garment image retrieval featured in satisfactory performance is proposed. In this study, the Grab Cut auto segmentation algorithm is applied first to segment garment images and extract the image’s foreground. And then, the color coherence vector (CCV) and the dominant color method are adopted to extract the color features to conduct garment image retrieval. The experimental data show that the Grab Cut auto segmentation algorithm is capable of extracting the foreground of garment images with either simple or complex background. Meanwhile, the data also indicate that compared with the garment image retrieval by extracting color features using CCV, extracting color features by dominating color method shows both higher accuracy and recall rates.

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-Based Image Retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results in the order 95.4%, 88.2%, 84.4% and 96.05% for Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depicts an experimental and analytical analysis on different color feature descriptors namely, Color Moment (CM), Color Auto-Correlogram (CAC), Color Histogram (CH), Color coherence vector (CCV) and Dominant Color Descriptor (DCD). The proposed hybrid Color Descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade Forward Back Propagation Neural Network (CFBPNN) used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

Classification of Grain Storage Inventory Modes Based on Temperature Contour Map of Grain Bulk Using Back Propagation Neural Network

Inventory modes classification can reduce the workload of grain depot management and it is time-saving, not labor-intensive. This paper proposed a method of using a temperature contour map converted from digital temperature data to classify stored grain inventory modes in a large bulk grain warehouse, which mainly included detection of inventory changes and routine operations performed (aeration). The back propagation (BP) neural network was used in this method to identify and classify grain storage inventory modes based on the temperature contour map for helping grain depot management work. The method extracted and combined color coherence vector (CCV), texture feature vector (TFV) and smoothness feature vector (SFV) of temperature contour maps as the input vector of the BP neural network, and used inventory modes as the output vector. The experimental results indicated that the accuracy of the BP neural network with vector (CCV and TFV and SFV) as the input vector was about 93.9%, and its training time and prediction time were 320 and 0.12 s, respectively.

Content-based Fish Classification Using Combination of Machine Learning Methods

Fish species recognition is an increasing demand to the field of fish ecology, fishing industry sector, fisheries survey applications, and other related concerns. Traditionally, concept-based fish specifies identification procedure is used. But it has some limitations. Content-based classification overcomes these problems. In this paper, a contentbased fish recognition system based on the fusion of local features and global feature is proposed. For local features extraction from fish image, Local Binary Pattern (LBP), Speeded-Up Robust Feature (SURF), and Scale Invariant Feature Transform (SIFT) are used. To extract global feature from fish image, Color Coherence Vector (CCV) is used. Five popular machine learning models such as: Decision Tree, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Naïve Bayes, and Artificial Neural Network (ANN) are used for fish species prediction. Finally, prediction decisions of the above machine learning models are combined to select the final fish class based on majority vote. The experiment is performed on a subset of ‘QUT_fish_data’ dataset containing 256 fish images of 21 classes and the result (accuracy 98.46%) shows that though the proposed method does not outperform all existing fish classification methods but it outperforms many existing methods and so, the method is a competitive alternative in this field.

Analysis of methods of image feature extraction

The article considers the methods of extraction and representation of image features, which play an important role in the processing of multimedia data. An overview of the latest developments in the field of image feature extraction is performed: extraction of color features from images where color has many functions, such as color moments(CM), color coherence vector (CCV), etc., extraction of texture features using the Gabor filter and extraction of shape features using methods based on contours and regions. An overview of the methods for representing image features is performed. The efficiency of merging global and local features in image processing was analyzed, as well as another way of presenting features like a visual word package. In the course of further work, the authors plan to study the relationship between the number of functions and the final performance. Intuitively, the larger the number of functions, the better the final performance. Secondly, the study of the relationship between the representation of functions and the final performance is also a very interesting and complex topic. It includes methods for representing objects (global, block, and regional). In the case of using block and regional functions, it is necessary to investigate the performance, which depends on the size of the partition or segmentation. Third, it is supposed to investigate the relationship between a suitable combination of image functions and the final performance in order to assess whether this combination can further improve performance. The results of the research are supposed to be used in the geolocation system of photos based on the automatic analysis of their images. Improving the accuracy and performance of image feature extraction will help improve the quality of the geopositioning system proposed in the article [28].

Automatic vegetable disease identification approach using individual lesion features

This paper proposes an automated approach for plant disease identification at leaf surface level using optimized features extracted from pathologically localized disease lesion areas. The automatic identification is achieved using machine learning classifiers built on the features. The disease region is mainly characterized as the combination of two symptom regions, the chlorotic and necrotic lesions that convey distinct characterization patterns. Typical machine learning approaches extract these patterns globally from the disease region as a whole region of interest (ROI), which often results in 10 s to 100 s of redundant features in the final feature vector that degrades the identification performance. Consequently, other methods resolve to use complex optimization algorithms to perform feature selection, which further compounds on the method complexity often without meaningful improvement. This study presents a localized feature extraction method from the individual chlorotic and necrotic lesions minimizing feature redundancy and vector size. Color coherence vector (CCV), a feature that portrays distinct homogeneous patterns relative to the disease progression are extracted from the chlorotic region. On the other hand, local binary pattern (LPB) is extracted from the necrotic region. Concatenating these individual lesion features results in a pathological feature vector for disease identification, thus minimizing feature size and avoiding the potentiality of dealing with descriptors that are superficial. To test the effectiveness of the proposed method, different conventional classifiers are used to test the quality and efficiency of these features in accurately classifying the plant diseases. The efficacy of the proposed approach is confirmed with the achieved high precision and recall rates of over 99% in all cases, and an improved accuracy compared to other reported works. The proposed work in this paper finds application in simplified decision support systems for the automation of plant disease identification and other resource management practices in the field of precision agriculture.

Color Based Oil Palm Crop Bunch Grading Using Probabilistic Neural Network

This work aims at grading the oil palm crop bunch in to three categories unripe, ripe and overripe. Different color feature models like color histogram, color moments, color correlogram and color coherence vector are used to extract the color features of the crop bunch. Oil palm crop bunches are classified into above mentioned grades using Probabilistic Neural Network. Experimentation is carried out using image dataset of 300 RGB images across three categories. An accuracy of 98.33% is achieved with 70% training, 10% validation and 20% testing for Color Coherence Vector features.

Leaf Disease Detection Based on Local Gabor Binary Pattern Histogram Sequence and Neural Network

Agriculture forms the main source of food in India, especially in the southern area. The economy of India directly depends on agriculture plants. But due to some major diseases such as blast, brown spot, and bacterial blight, there is a reduction in plant growth which greatly affects agricultural productivity. The farmers add irrelevant pesticides with their limited knowledge which will degrade the quality of the crop but also degrade the soil quality. In the proposed method Machine Vision techniques based on neural networks are used to detect plant health or diseases indicated by leaf anomaly. Image processing algorithms such as K means clustering is used to segment affected areas. From the segmented images of the plant leaf, features are extracted using Color Coherence Vector (CCV) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS). The extracted features are fed as input to a backpropagation neural network to classify the unhealthy leaf.

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach.

In medicine, a misdiagnosis or the absence of specialists can affect the patient’s health, leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of specialists in otolaryngology in third world countries forces patients to seek medical attention from general practitioners, whom might not have enough training and experience for making correct diagnosis in this field. To tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis, earwax plug, and chronic otitis media. To characterize the tympanic membrane and ear canal for each condition, we implemented three different feature extraction methods: color coherence vector, discrete cosine transform, and filter bank. We also considered three machine learning algorithms: support vector machine (SVM), k-nearest neighbor (k-NN) and decision trees to develop the ear condition predictor model. To conduct the research, our database included 160 images as testing set and 720 images as training and validation sets of 180 patients. We repeatedly trained the learning models using the training dataset and evaluated them using the validation dataset to thus obtain the best feature extraction method and learning model that produce the highest validation accuracy. The results showed that the SVM and k-NN presented the best performance followed by decision trees model. Finally, we performed a classification stage –i.e., diagnosis– using testing data, where the SVM model achieved an average classification accuracy of 93.9%, average sensitivity of 87.8%, average specificity of 95.9%, and average positive predictive value of 87.7%. The results show that this system might be used for general practitioners as a reference to make better decisions in the ear pathologies diagnosis.

Object based Image Retrieval with Segmentation and Extraction of Features using various Methods

This paper proposes Object Based Image Retrieval (OBIR) System with segmenting the objects from the images and then extracting various features from the objects. The objects are the most prominent part of an image which relates more to the human perception. First, the object present in the images is segmented by four different segmentation techniques such as K-means, Active Contours, Edge-Convex hull and Global Thresholding. Later, the color features such as Color Histogram (CH) and Color Coherence Vector (CCV), Texture feature using Local Binary Patterns (LBP) and shape feature using Histogram of Gradients (HOG) are extracted. Finally, with the usage of different segmentation and techniques mentioned above feature are extracted from objects. Results obtained are tabulated and performance study is made.

Identification of Corrosion Degree for Grounded Screen of Extra High Voltage Transmission Line in Coastal Area Based on Infrared Thermal Image

Yao, N.; Wu, X., and Cai, Y., 2018. Identification of corrosion degree for grounded screen of extra high voltage transmission line in coastal area based on infrared thermal image. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 124–128. Coconut Creek (Florida), ISSN 0749-0208.In the traditional recognition methods of corrosion degree of grounding grid of extreme high voltage transmission line, the noise in the infrared thermal images causes the inaccurate corrosion position and the large recognition error. Therefore, the recognition method of corrosion degree based on the infrared thermal image was proposed. Through the color histogram method, the color coherence vector method and the color correlogram method, the extraction of corrosion area characteristics in grounding grid image of extreme high voltage transmission line was realized. On this basis, HSI color space conversion was used to obtain the infrared image edge information. The median filter was used to preserve the advantage of edge information. The gradient method was used to extract the highest temperature area, realizing the identification of corrosion area of grounding grid of extreme high voltage transmission line. Experimental results show that the proposed method can accurately locate the corrosion area. It has the high recognition accuracy.

A Comparative Study of Data Mining approaches for Bag of Visual Words Based Image Classification

Image classification is one of the most significant and challenging tasks in computer vision. The goal of this task is to build a system that is capable to reveal an image label within a collection of different image categories. This paper presents and discusses the application of various data mining techniques for image classification based on Bag of Visual Words (BoVW) feature extraction algorithm. The BoVW model is constructed using grey level features: The Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) descriptors along with color features: Color correlograms and Improved Color Coherence Vector (ICCV). Five data mining techniques; Neural Networks (NN), Decision Trees (DT), Bayesian Network (BN), Discriminant Analysis (DA) and K Nearest Neighbor (KNN), are explored and evaluated on two large different datasets: Corel-1000 and COIL-100. The experimental results illustrate that BN and DA outperform the other data mining methods considered in this comparative study. For Corel-1000 dataset, BN and DA achieved an average accuracy and specificity of about 99.9% and an average sensitivity of about 99.5 and 99.4%, respectively. While for the COIL-100 dataset, BN and DA accomplished an average accuracy and sensitivity of about 100% and an average specificity of about 98.5 and 98.9, respectively.

Fuzzy and Neural Network based Tomato Plant Disease Classification using Natural Outdoor Images

Objectives: The aim of the study is to automate the plant disease recognition and classification process by using image processing and soft computing techniques. Methods/Analysis: The proposed method examined the five types of tomato plant diseases using natural outdoor images in the study. The tomato plant images categorized into six categories including five disease infected that are bacterial leaf spot, fungal septoria leaf spot, bacterial canker, fungal lateblight, tomato leaf curl and one non-infected (healthy). The total 180 images of the dataset used for training and testing purpose. The total thirteen features computed by using CIE XYZ color space conversions that included color moments, histogram, and color coherence vector features. For classification, computed features are fed into three classifiers, i.e., “Fuzzy Inference System based on subtractive clustering”, “Adaptive neuro-fuzzy inference system using hybrid learning algorithm and multi-layer feed forward back propagation neural network” for classification of six injured and healthy tomato plant disease. Finding: The classification accuracy is best yielded with multi-layer feed forward back propagation classifier of 87.2%. Novelty/Improvement: Usually, in the studies the only one type of plant disease considered for the recognition and classification purpose. The current study considered five different types of tomato plant diseases including fungal, bacterial and viral. It indicates that the proposed algorithm could reliably classify the different types of plant diseases in digital images.

Image Classification Using Fusion of Holistic Visual Descriptions

An efficient approach for scene classification is necessary for automatically labeling an image as well as for retrieval of desired images from large scale repositories. In this paper machine learning and computer vision techniques have been applied for scene classification. The system is based on feature fusion method with holistic visual color, texture and edge descriptors. Color moments, Color Coherence Vector, Color Auto Correlogram, GLCM, Daubechies Wavelets, Gabor filters and MPEG-7 Edge Direction Histogram have been used in the proposed system to find the best combination of features for this problem. Two state-of- the-art soft computing machine learning techniques: Support vector machine (SVM) and Artificial Neural Networks have been used to classify scene images into meaningful categories. The benchmarked Oliva-Torralba dataset has been used in this research. We report satisfactory categorization performances on a large data set of eight categories of 2688 complex, natural and urban scenes. Using a set of exhaustive experiments our proposed system has achieved classification accuracy as high as 92.5% for natural scenes (OT4) and as high as 86.4% for mixed scene categories (OT8). We also evaluate the system performance by predictive accuracy measures namely sensitivity, specificity, F-score and kappa statistic.

Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words

Content-Based Image Retrieval (CBIR) has received an extensive attention from researchers due to the rapid growing and widespread of image databases. Despite the massive research efforts consumed for CBIR, the completely satisfactory results have not yet been attained. In this article, we offer a new CBIR technique that relies on extracting Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) feature descriptors as well as the color features; color correlograms and Improved Color Coherence Vector (ICCV). These features are joined and used to build a multidimensional feature vector. Bag-of-Visual-Words (BoVW) technique is utilized to quantize the extracted feature vector. Then, a multiclass Support Vector Machine (SVM) is implemented to classify the query images. The performance of the presented retrieval framework is analyzed and scrutinized by comparing it with three alternative approaches. The first one is based on extracting SURF descriptors while the second one is based on extracting SURF descriptors, color correlograms and ICCV. The third approach, on the other hand, is based on extracting MSER, color correlograms and ICCV. All implemented schemes are tested on two benchmark datasets; Corel-1000 and COIL-100 datasets. The empirical results show that our suggested approach has a superior discriminative classification and retrieval performance with respect to other approaches. The proposed method achieves average precisions of 88 and 93% for the Corel-1000 and COIL-100 datasets, respectively. Moreover, the proposed system has shown a substantial advance in the retrieval precision when compared with other existing systems.

An Interactive Approach for Retrieval of Semantically Significant Images

Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between lowlevel primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations.

Image Retrieval by Utilizing Structural Connections within an Image

Content-based image retrieval (CBIR) is broadly applicable for searching digital images from a gigantic database. Images are retrieved by their primitive visual contents such as color, texture, shape, and spatial layout. The approach presented in this paper utilizes structural connections within an image by integrating textured color descriptors and structure descriptors to retrieve semantically significant images. The retrieval results were obtained by applying the HSV histogram, color coherence vector, and local binary pattern histogram to the standard database of Wang et al., which has 1000 images of 10 different semantic categories. Euclidean distance was used to find the similarity between the query image and database images. This method was evaluated against different methods based on edge histogram descriptors, color structure descriptors, color moments, the color histogram, the HSV histogram, Tamura features, edge descriptors, geometrical shape attributes, and statistical properties such as mean, variance, skewness, and kurtosis. Retrieval results obtained using the proposed methods demonstrated a significant improvement in the average precision (73.8% and 73.1%) compared with those obtained using other existing retrieval methods. Index Terms—Content-based image retrieval, color coherence vector, local binary pattern, semantic gap, precision.

IJIREEICE - Electrical, Electronics, Instrumentation and Control

Content Based Visual Information Retrieval is one of the most cardinal and crucial aspect in the category of digital image processing. The significant mechanism that has been lucidly highlighted here is the fast multiresolution image querying using Color Coherence Vector (CCV) along with the refinement of color histogram. However, the retrieval speed and accuracy can be enhanced using color characteristics and mathematical model despite the collection of innumerable images in dataset. Color feature extraction and coherency estimation assist in flawless and swiftest image querying according to the matched template (matrices) of the color histogram image with maximum precision.

COMPARATIVE STUDY OF CONTENT BASED IMAGE RETRIEVAL TECHNIQUES FOR BACTERIAL CELLS CLASSIFICATION

Abstract A major challenge in microbial ecology is to develop reliable and facile methods of computer assisted microscopy that can analyze digital images of complex microbial communities at single cell resolution, and compute useful quantitative characteristics of their organization and structure without cultivation. This paper performs a fair comparative study of three most common feature extraction methods for content based bacterial image classification; Color Coherence Vector (CCV), Gray Level Co-occurrence Matrix (GLCM) and Color Moments. The comparison is made on the basis of these parameters: Run time, CPU time, Recall, F-measure and Precision measures. Performance of Feature Extraction techniques based on the number of retrieved images, Run time and CPU time. Experiments results are given to analyses the effectiveness of each techniques and how that Color Moments technique is more suitable than other techniques. Keywords: Bacteria - Image processing – CBIR-CCV- GLCM- Color Moments.