Color Autocorrelogram Research Articles

Content‐based image retrieval using a fusion of global and local features

AbstractColor, texture, and shape act as important information for images in human recognition. For content‐based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance. However, there have not been many powerful methods for combining all color, texture, and shape features. This study proposes a content‐based image retrieval method that uses the combined local and global features of color, texture, and shape. The color features are extracted from the color autocorrelogram; the texture features are extracted from the magnitude of a complete local binary pattern and the Gabor local correlation revealing local image characteristics; and the shape features are extracted from singular value decomposition that reflects global image characteristics. In this work, an experiment is performed to compare the proposed method with those that use our partial features and some existing techniques. The results show an average precision that is 19.60% higher than those of existing methods and 9.09% higher than those of recent ones. In conclusion, our proposed method is superior over other methods in terms of retrieval performance.

IR-MF-SVMe: Image Retrieval using Multiple Features Extraction with Support Vector Machine ensemble

Image representation is a widespread strategy of image retrieval based on appearance, shape information. The traditional feature representation methods ignore hidden information that exists in the dataset samples; it reduces the discriminative performance of the classifier and excludes various geometric and photometric variations consideration in obtaining the features; these degrade retrieval performance. Hence, proposed multiple features fusion and Support Vector Machines Ensemble (IR-MF-SVMe); an Image Retrieval framework to enhance the performance of the retrieval process. The Color Histogram (CH), Color Auto-Correlogram (CAC), Color Moments (CM), Gabor Wavelet (GW), and Wavelet Moments (WM) descriptors are used to extract multiple features that separate the element vectors of images in representation. The multi-class classifier is constructed with the aggregation of binary Support Vector Machines, which decrease the count of false positives within the interrelated semantic classes. The proposed framework is validated on the WANG dataset and results in the accuracy of 84% for the individual features and 86% for the fused features related to the state-of-the-arts.

Classification of plant diseases using machine and deep learning

Abstract Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Naïve Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) is supposed to possess tremendous potential in enhancing the accuracy. This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets to solve binary and multiclass subcategories of plant diseases. Using the proposed model, results achieved are better, obtaining 99.4% accuracy and 99.9% sensitivity for binary class and 99.2% accuracy for multiclass. It is proven that the proposed model outperforms other approaches, namely LibSVM, SMO (sequential minimal optimization), and DL with activation function softmax and softsign in terms of F-measure, recall, MCC (Matthews correlation coefficient), specificity and sensitivity.

Comparison of color-based feature extraction methods in banana leaf diseases classification using SVM and K-NN

In Malaysia, banana is a top fruit production which contribute to the economy growth in agriculture field. Hence, it is significant to have a quality production of banana and important to detect the plant diseases at the early stage. There are many types of banana leaf diseases such as Banana Mosaic, Black Sigatoka and Yellow Sigatoka. These three diseases are related to color changes at banana. This research paper is an experiment based and need to identify the best color feature extraction method to classify banana leaf diseases. Total of 48 banana leaf images that are used in this research paper. Four types of color feature extraction methods which are color histogram, color moment, hue, saturation, and value (HSV) histogram and color auto correlogram are experimented to determine the best method for banana leaf diseases classification. While for the classifiers, support vector machine (SVM) and k-Nearest neighbors (k-NN) are used to evaluate the performance and accuracy of each color feature extraction methods. There are also preliminary experiments to identify accurate parameters to use during classification for both classifiers. Our experimental result express that HSV histogram is the best method to classify banana leaf diseases with 83.33% of accuracy and SVM classifier perform better compared to k-NN.

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.

An efficient method to classify GI tract images from WCE using visual words

The digital images made with the Wireless Capsule Endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the CS-LBP (Center Symmetric Local Binary Pattern) and the ACC (Auto Color Correlogram) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve this issue, we suggested a Visual Bag of Features(VBOF) method by incorporating Scale Invariant Feature Transform (SIFT), Center-Symmetric Local Binary Pattern (CS-LBP) and Auto Color Correlogram (ACC). This combination of features is able to detect the interest point, texture and color information in an image. Features for each image are calculated to create a descriptor with a large dimension. The proposed feature descriptors are clustered by K- means referred to as visual words, and the Support Vector Machine (SVM) method is used to automatically classify multiple disease abnormalities from the GI tract. Finally, post-processing scheme is applied to deal with final classification results i.e. validated the performance of multi-abnormal disease frame detection.

Image Classification based on CBIR

In this paper, we present a new way to classify four types of images (Car accidents, Fire, Abnormal objects in street and Digs) which will be sent to four government places; Civil Defence, police station and Municipal. The classification method depends on the Content-Based Image Retrieval (CBIR), where we use a new method. In this method, we use a combination of three methods to extract features from an image; Single Value Decomposition (SVD), Edge Histogram Descriptor (EHD) and Color Auto-Correlogram for Extraction Features. You will use these features to find the closest similarities to the query image from the database images by selecting the closest 3 images, then choosing the class to which the closest two images belong to the retrieved. The combined method showed 100% accuracy in training phase and 100% test phase accuracy.

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

In this paper, a three stage hierarchical image retrieval scheme using a color, texture and shape visual contents (or descriptors) is proposed, since single visual content is not produce an adequate retrieval results effectively. This scheme has reduced the searching space during the image retrieval process at a certain extent due to the hierarchical mode. In initial stage, the shape feature descriptor has been computed by simple fusion of histograms of gradients and invariant moments of segmented image planes. The shape based retrieval process has reduced the search space by discarding the non-relevant images from the universal dataset (or original dataset) effectively and kept the retrieved images into the intermediate dataset. In the second stage, the texture feature descriptors have been computed from the intermediate sub-image dataset by applying the adaptive tetrolet transform on image plane of preprocessed HSV color image. This transform provides the multi-resolution images with finer details by employing the tetrominoes and the proper arrangement of tetrominoes contributes the effective local geometry of image plane. The gray level co-occurrence matrix based texture feature descriptor is obtained by computing second order statistical parameters from each decomposed sub-image. At this stage, the most of the irrelevant images are discarded by retrieving the images from intermediate dataset but still some undesired images are left, those will be handled at the last stage. At this stage, fused color information is captured by applying the color autocorrelogram on both the non-uniform quantized color components of the preprocessed HSV color image. Finally, the color feature descriptor produces the desired retrieval results by discarding the irrelevant images from the texture based sub-image dataset. The proposed scheme has also low computational overhead due to the use of three descriptors at different stages separately. The retrieved results show the better accuracy as compared to the other related visual contents based image retrieval schemes.

Content-based image retrieval using combined texture and color features based on multi-resolution multi-direction filtering and color autocorrelogram

This paper proposes content-based image retrieval using combined feature extraction method by local energy, rotation-invariant uniform local binary patterns (RULBP) and color autocorrelogram. In this paper, features of local energy and RULBP are extracted in a high frequency domain of a gray image, using multi-resolution multi-direction (MRMD) filtering. Autocorrelogram is used in a two-dimensional histogram of hue and saturation color spaces. The Mahalanobis distance is used in measuring the degree of similarity of features. Evaluation of the experimental result of the image retrieval is based on precision and recall. The tested DBs (databases) are Corel and VisTex; Corel_MR and VisTex_MR of resolution variants; and Corel_MD and VisTex_MD of direction variants of first two DBs, respectively. The result of the experiment shows that the proposed method is superior to compared methods in the aspect of retrieval performance to retrieve MRMD images.

Prediction of Esophagitis and Z-Line from Wireless Capsule Endoscopy images using Fusion of low-level features

Wireless endoscopy capsule (WCE) pictures areoften used to recognize digestive tract illnesses as they enable immediate GI tract perception. In any case, it requires a clinician's long-lasting review because of an incredible number of pictures delivered by the system. In this manner, it might be valuable to devise an Automatic detection framework to enable clinicians to distinguish abnormal pictures. In this work, it is endeavour to plan an electronic plan intending to distinguish esophagitis in WCE pictures. The Esophagitis in WCE pictures show extraordinary variations in appearance. Scale-Invariant Feature Transform (SIFT) and Auto Color Correlogram (ACC) are two features that are used to coordinate the texture, color and shape characteristics collected from points of interest. Using Naïve Bayes, Support Vector Machine (SVM) and Random Forest, we assessed the performance with comprehensive tests on our current picture information consisting of 100 normal-z-line WCE pictures and 100 esophagitis. From the experimental analysis, it is promising to use the proposed plan to distinguish esophagitis and normal-z-line from WCE pictures.

Integration of Statistical Based Texture and Color Feature for Medical Image Retrieval

Today, the common problem in the domain of computer vision and pattern recognition is content based image retrieval (CBIR). In this paper, a novel image retrieval method using the geometric details based on the correlation among edgels and correlation between pixels has been introduced. The autocorrelation based choridiogram descriptor has been extracted from the image to obtain geometric, texture and spatial information. Color autocorrelogram has been computed to obtain color, texture and spatial information. The proposed method is tested on benchmark heterogeneous medical image database and LIDC-IDRI-CT and VIA/I-ELCAP-CT databases and results are compared with typical CBIR system for medical image retrieval.

Soil texture classification using multi class support vector machine

The objective of this study is to process the soil images to generate a digital soil classification system for rural farmers at low cost. Soil texture is the main factor to be considered before doing cultivation. It affects the crop selection and regulates the water transmission property. The conventional hydrometer method determines the percentage of sand, silt, and clay present in a soil sample. This method is very cost and time-consuming process. In this approach, we collect 50 soil samples from the different region of west Guwahati, Assam, India. The samples are photographed under a constant light condition using an Android mobile of 13 MP cameras. The fraction of sand, silt, and clay of the soil samples are determined using the hydrometer test. The result of the hydrometer test is processed with the United State Department of Agriculture soil classification triangle for the final soil classification. Soil images are processed through the different stages like pre-processing of soil images for image enhancement, extracting the region of interest for segmentation and the texture analysis for feature vector. The feature vector is calculated from the Hue, Saturation, and Value (HSV) histogram, color moments, color auto Correlogram, Gabor wavelets, and discrete wavelet transform. Finally, Support Vector Machine classifier is used to classify the soil images using linear kernel. The proposed method gives an average of 91.37% accuracy for all the soil samples and the result is nearly the same with the United State Department of Agriculture soil classification.

Pixel based Hybrid Copy Move Image Forgery Detection using Zernike Moments and Auto Color Correlogram

All of us rely on the images for the memories of our life and loved ones. The images are useful in proving an evidence of the event. Image forgery has become much prominent nowadays and is being done either for fun or intentionally. In this paper, we detect copy move forgery by combining the two features namely, Zernike moments and auto colour correlogram. The first checks the shape of the objects in the block, the latter for distance of each colour pixel taking into account the 64 colours. Both combine together to identify the regions for which copy-move forgery exists. The first completes the number of comparisons in the order of n2, where n is the number of blocks. The ACC verifies the colours used in the block. If both matches are confirmed, the suspicious blocks are returned. The method out-performs the existing methods based on the probability approach.

Building Recognition Based on Sparse Representation of Spatial Texture and Color Features

In this paper, we presented a novel building recognition method based on a sparse representation of spatial texture and color features. At present, the most popular methods are based on gist features, which can only roughly reflect the spatial information of building images. The proposed method, in contrast, uses multi-scale neighborhood sensitive histograms of oriented gradient (MNSHOGs) and color auto-correlogram (CA) to extract texture and color features of building images. Both the MNSHOG and the CA take spatial information of building images into account while calculating texture and color features. Then, color and texture features are combined to form joint features whose sparse representation can be dimensionally reduced by an autoencoder. Finally, an extreme learning machine is used to classify the combined features after dimensionality reduction into different classes. Several experiments were conducted on the benchmark Sheffield building dataset. The mean recognition rate of our method is much higher than that of the existing methods, which shows the effectiveness of our method.

Greedy Learning of Deep Boltzmann Machine (GDBM)’s Variance and Search Algorithm for Efficient Image Retrieval

Despite extensive research on content-based image retrieval, challenges such as low accuracy, incapability to handle complex queries and high time consumption persist. Initially, a preprocessing technique is introduced in this study, a technique that uses a median filter to remove noise to achieve improved accuracy and reliability. Then, Fourier and circularity descriptors are extract in an effective manner correspondent to the texture and affine shape adaptation features. In addition, various descriptors, such as color histogram, color moment, color autocorrelogram and color coherency vector, are extracted as the invariant color features. The multiple ant colony optimization (MACOBTC) approach is implemented with whole features to find relevant features. Finally, the relevant features are utilized for the greedy learning of deep Boltzmann machine classifier (GDBM). The proposed approach obtains effective performance and accurate results on four datasets and is analyzed with various parameters such as accuracy, precision, recall, Jaccard, Dice, and Kappa coefficients. The GDBM provides a 25% increase in accuracy compared with existing techniques, such as the a priori classification algorithm.

Flower Image Retrieval Using Color and Shape Features and Multiple Distance Functions

It is noticeable that when flower images are observed, flower’s color signifies people and often people ignore small and isolated sectors of the image. So for retrieving flower images color feature of the images is very important. Different flowers have also different shapes. So for differing flower images from another flower images shape feature is also a significant feature. In this paper, we proposed a method of retrieving flower images using their color and shape feature. We used HSV color histogram, color moments and color auto correlogram descriptors for describing color feature and HOG descriptor for describing shape feature. We calculate the similarities between the query image and the database images by employing a set of distance functions. We evaluated our result for our own dataset of 15 categories flower images.

A Novel Hybrid Framework for Medical Image Retrieval

A new hybrid framework for Content-Based Medical Image Retrieval (MCBIR) is proposed in this paper to deals with the accuracy issues related with the existing MCBIR. The proposed hybrid framework initially divides the images into number of non-overlapping rectangular regions. Subsequently, statistical based color autocorrelogram (CA) and texture autocorrelogram (TA) is extracted for each region respectively. Then the geometric based chordiogram descriptor (CD) is extracted for each region. Both the statistical based and geometric based descriptors are combined to create a feature vector. The corresponding image regionsor patches in the query and target medical images are compared using the Canberra distance measure. The proposed hybrid framework is evaluated using the benchmark database and it is confirmed that it significantly outperforms the state-of-the-art system in terms precision, recall and G-measure.

Translated Trademarks Retrieval using Color Autocorrelogram for Extracted Textual Parts

This article describes a trademark retrieval system using autocorrelogram features. The proposed algorithm deals with the trademarks of a same company having different shapes in different languages. Textual and mixed logos types are considered in this study, in which the text area is located based on an edge detection algorithm and connected components merging strategy to form the bounding box enclosing the textual parts of a logo. Color autocorrelogram is applied on the extracted and quantified text area. An adapted evaluation metric is proposed to measure the performance of the proposed technique. Results show a good retrieval rate on real trademark images.

Subcategory-Aware Feature Selection and SVM Optimization for Automatic Aerial Image-Based Oil Spill Inspection

Oil spill inspection is critical to the marine and coastal ecosystems, and has been widely studied by various remote sensing technologies, such as synthetic aperture radar and hyperspectral. To improve the temporal resolution and the inspection flexibility, we propose a novel aerial image-based system that can find oil spills timely from images captured using onboard optical cameras installed in unmanned aerial vehicle or airplanes. In particular, a subcategory-aware feature selection (FS) and support vector machine (SVM) joint optimization technique is proposed to learn a discriminative model that can tell the existence of oil spills within an optical image of the marine surface. A set of color-based features is first extracted and concatenated together to characterize the oil spill incidence in an image, where a new color autocorrelogram is designed, which can better describe each color’s spatial distribution in an image. Furthermore, subcategory-aware joint FS and SVM optimization technique is designed, which is capable of generating the optimal feature subset and SVM component models. Experiments on a set of real-world marine surface images show that the proposed technique outperforms the state-of-the-art techniques and achieves promising results for aerial image-based oil spill inspection.

A learning automata framework based on relevance feedback for content-based image retrieval

The need for efficient image browsing and searching motivates the use of Content-Based Image Retrieval (CBIR) systems. However, they suffer from a big gap between high-level image semantics and low-level features. So, a learning process to reduce the gap seems quite useful. This paper presents a novel Learning Automata (LA)-based approach to improve the CBIR systems. Distributed Learning Automata (DLA) is used in this work to learn the relevant images from textual query feedbacks of the users. Subsequently, the retrieved images are ranked according to the learning outcome and similarity measure. In this study, the similarity between images is evaluated based on two color descriptors: the global color histogram and local color auto-correlogram. A thorough observation and comparison of these color descriptors performances are performed with different color spaces and also with various similarity measures. Experimental results on two publicly available databases demonstrate that the performance of the proposed CBIR system after each round is improved and the system could retrieve images compatible with the users’ perception.