RGB Color Histogram Research Articles

Digital image processing combined with machine learning: A new strategy for brown sugar classification

The coloring of foods is one of the main attributes of importance for consumers and it can be decisive for a consumer to accept or reject the product. Models that explore brown sugar coloring are scarce in scientific research. So, a new strategy for brown sugar classification through the combination of digital image processing, machine learning and physicochemical composition data was proposed. RGB channel intensities and color histogram data, obtained from digital image processing, in combination with some physicochemical characteristics (sucrose, Ca, Fe, ICUMSA color and total phenolic compounds (TPC)) were used as training and external validation datasets in the creation of classification models by RF algorithm. Excellent performance of classification models was observed by high overall accuracy rates for ICUMSA color (92.6 %), Ca and sucrose (100 %), Fe (94.9 %), and TPC (97.6 %). Thus, classifying brown sugar based on its color can be a valuable strategy for the beverage and food industries, allowing for greater diversification and meeting consumer needs while enhancing the quality and consistency of products.

Shot Boundary Detection Based on Global Features and the Target Features

Video processing plays an important role in the intelligent monitoring and management system of agricultural information. Video shot boundary detection is the basic symmetry step underlying video processing techniques. According to the current shot boundary detection algorithm, the feature changes between gradual transition frames are difficult to detect, and the misdetection situation is caused by ignoring the attention of the target feature during the feature extraction. A novel symmetry multi-step comparative scheme of shot boundary detection algorithm based on global features and target features is proposed. First, the RGB color histogram features of the video frame are extracted. Second, foreground object detection for the video frames is performed using the Gaussian Mixture Model (GMM), and the scale-invariant features transformation (SIFT) of the foreground targets is extracted. Finally, global features and target features fusion through weights, calculating the difference between adjacent frames across multiple steps, generate a pattern distance map. The pattern distance map of the gradual transition and the cut detection is different; we can judge the gradual transition and the cut detection according to the pattern distance map. Experiments show that the proposed symmetry method improves by about 2% in recall and accuracy compared to other algorithms.

Analysis Manipulation Copy-Move on Image Digital using SIFT Method and Histogram Color RGB

The application of the SIFT (Scale Invariant feature transform) algorithm and the RGB color histogram in Matlab can detect the suitability of objects in digital images and perform tests accurately. In this study, we discuss the implementation to obtain object compatibility on digital images that have been manipulated using the SIFT Algorithm method on the Matlab source, namely by comparing the original image with the manipulated image. The suitability of objects in digital images is obtained from the large number of keypoints obtained, other additional parameters, namely comparing the number of pixels in the analyzed image, as well as changes in the histogram in RGB color in each analyzed image. The purpose of this research is how to apply the SIFT (Scale Invariant feature transform) Algorithm and RGB color histogram to detect the suitability of objects in digital images and perform tests accurately. This study discusses the implementation to obtain object compatibility in digital images that have been manipulated using the SIFT Algorithm method on Matlab sources, namely by comparing the original image with the manipulated image. The suitability of objects in digital images is obtained from the large number of keypoints obtained, other additional parameters, namely comparing the number of pixels in the analyzed image, as well as changes in the histogram in RGB color in each analyzed image

Hierarchical Clustering for Image Classification in Dermatology: Towards Mobile Deploying.

In the context of increasing interest in computer-assisted diagnosis for skin lesion images and mobile applications to be used in real life settings, we propose a combined desktop-smartphone solution for dermatological image classification. Hierarchical agglomerative and divisive clustering are both implemented as methods of cluster analysis, with the RGB color histogram as descriptor for a global image analysis. The cosine similarity is employed for classifying the query image in one of the available clusters, characterized by their centroids. The solution has been tested with a public database of dermoscopic images, with an overall accuracy of 0.73, 95%CI (0.58;0.85).

Social media video summarization using multi-Visual features and Kohnen's Self Organizing Map

Social networking tools such as Facebook, YouTube, Twitter, and Instagram, are becoming major platforms for communication. YouTube as one of the primary video sharing platform serves over 100 million distinct videos, 300 hours of videos are uploaded on YouTube every minute along with textual data. This massive amount of multimedia data needs to be managed with high efficiency, the irrelevant and redundant data needs to be removed. Video summarization ideals with the problem of redundant data in a video. A summarized video contains the most distinct frames which are termed as key frames. Most of the research work on key frames extraction considers only a single visual feature which is not sufficient for capturing the full pictorial details and hence affecting the quality of video summary generated. So there is a need to explore multiple visual features for key frames extraction. In this research work a key frame extraction technique based upon fusion of four visual features namely: correlation of RGB color channels, color histogram, mutual information and moments of inertia is proposed. Kohonen Self Organizing map as a clustering approach is used to find the most representative frames from the list of frames coming after fusion. Useless frames are discarded and frames having maximum Euclidean distance within a cluster are selected as final key frames. The results of the proposed technique are compared with the existing video summarization techniques: User generated summary, Video SUMMarization (VSUMM), and Video Key Frame Extraction through Dynamic Delaunay Clustering (VKEDDCSC) which shows a considerable improvement in terms of fidelity and Shot Reconstruction Degree (SRD) score.

Comparative study of two imputation methods in R package for RGB color histogram data

Multiple imputation (MI) is a powerful tool in handling missing data issue. This paper provides a comparison of the multiple imputation method in Amelia II package and MICE package in R. Both packages are well-known and incredible to conduct the missing data research in numerous domains. There are very limited researches comparing the multiple imputation combined with other techniques in the image data context. We employ the mean absolute error (MAE) error metric to evaluate the accuracy on the predicted values based on 20% and 50% of missing data percentages. Although the implementation of MICE is time consuming, the result shows that MICE can deal with large amount of missing values while Amelia II is only capable to deal up 20% amount of missing values. Based on the MAE result, both packages show that they are superior on the particular variables.

Thermal infrared and visible sequences fusion tracking based on a hybrid tracking framework with adaptive weighting scheme

Object tracking based on single sensor image sequences is now proved to be insufficient when facing complex challenging factors such as occlusions, background clutter, illumination variations, deformation and scale change. Complementary information between thermal infrared and visible image sequences is highly valuable and plays a critical role in tracking under complex scenarios. Previous fusion-before-tracking algorithms are not efficient and accurate enough due to the inevitable introduction of redundant information and considerable computational consumption. In this paper, we propose a robust fusion tracking method that exploits the abovementioned complementary information under a hybrid “tracking-by-detection” framework which consists of two tracking modules—the correlation filter based tracking (CFT) module and histogram based tracking (HIST) module. In CFT module, features extracted from both thermal infrared and visible images such as histogram of oriented gradient (HOG), image intensity and color names, are utilized to generate response maps and then adaptively fused through a denoising fusion scheme. In HIST module, a response map is obtained by adopting RGB color histogram in a statistical tracking model. Then, the response maps of two modules are fused via a new adaptive weighting scheme we proposed. Extensive experimental results on challenging thermal infrared and visible image sequences demonstrate the accuracy and robustness of the proposed method in comparison with several state-of-the-art methods.

Classification of melanoma based on feature similarity measurement for codebook learning in the bag-of-features model

Bag-of-features (BoF) model based melanoma classification methods can effectively assist dermatologists to diagnose skin diseases. Codebook learning is a key step in the BoF model and the k-means clustering algorithm is often used to learn a codebook. However, the cluster centers generated by k-means algorithm are irresistibly attracted to the denser regions. This produces a suboptimal codebook in which most of the clusters are located in dense regions and a few are in sparse regions. Therefore, this can easily affect the classification accuracy. In this paper, we develop a novel methodology for classifying skin lesions. Firstly, we propose a new codebook learning algorithm based on feature similarity measurement (FSM) to effectively quantify the original features of melanomas. We utilize the combination of the linearly independent and linear prediction (LP) algorithms to measure feature similarity. Especially, the codewords learned by the proposed FSM algorithm are not affected by the density of samples. Therefore, a more discriminating BoF histogram for the melanoma classification is achieved. Secondly, we propose a melanoma classification method based on the FSM codebook learning algorithm. In particular, we adopt the BoF histogram fusion strategy of different feature descriptors, i.e., RGB color histogram and scale-invariant feature transform (SIFT). Finally, the experimental results show that the proposed melanoma classification method outperforms some state-of-the-art methods in terms of classification accuracy and efficiency. The results also show the performance of the proposed method is greatly improved by the use of the proposed codebook learning algorithm.

Classification of pork and beef meat images using extraction of color and texture feature by Grey Level Co-Occurrence Matrix method

In Indonesia, beef meat usually sells with expensive price. It because of long distribution from the cattle rancher to the end customer. This causing a high-cost on the production and distribution. This condition made some people trying to be tricky by mixing beef meat with another meat, such as pork. It occurs the financial loss for customers. Currently, the meat identification done manually using visual identification of human vision. This method has a lot of weaknesses to differ beef and pork meat due because it need expert to identify the difference. The improvement of science and technology in computer vision helps the customer to identify beef and pork meat automatically using its images. This research classification of beef meat and pork using back-propagation method for its classifier. The purpose of this study is identifying differences in beef and pork based on digital image. Color feature using first order statistic mean from its RGB color value and using Grey Level Co-Occurrence Matrix for texture analysis. Experiment result of classification of beef and pork meat using Back-propagation, RGB color histogram value, and Grey Level Co-Occurrence Matrix achieving 89,57% of accuracy result.

ANALISIS PENDETEKSI KECOCOKAN OBJEK PADA CITRA DIGITAL DENGAN METODE ALGORITMA SIFT DAN HISTOGRAM COLOR RGB

Through using tools of image processing on digital images just like gimp and adobe photoshop applications, an image on digital images can be a source of information for anyone who observes it. On one hand, those applications can easily change or manipulate the authenticity of the image. On the other hand, they can be misused to undermine the credibility of the authenticity of the image in various aspects. Thus, they can be considered as a crime. The implementation of the SIFT Algorithm (Scale Invariant feature transform) and RGB color histogram in Matlab can detect object fitness in digital images and perform accurate test. This study discusses the implementation of getting object fitness on digital image that has been manipulated by SIFT Algorithm method on the Matlab source. It is done by comparing the original image with the manipulated one. The object fitness in digital images can be obtained from a number of key points and other additional parameters through comparing number of pixels on the analyzed image and on the changed histogram in RGB color on each analyzed image. The digital image forensics which is known as one of the scientific methods commonly used in researches is aimed to obtain evidences or facts in determining the authenticity of the image on digital images. The use of the SIFT algorithm is chosen as an extraction method because it is invariant to scale, rotation, translation, and illumination changes. SIFT is used to obtain characteristics of the pattern of the gained key point. The tested result of the SIFT Algorithm method (Scale Invariant feature transform) is expected to produce a better image analysis.

Local mesh quantized extrema patterns for image retrieval.

In this paper, we propose a new feature descriptor, named local mesh quantized extrema patterns (LMeQEP) for image indexing and retrieval. The standard local quantized patterns collect the spatial relationship in the form of larger or deeper texture pattern based on the relative variations in the gray values of center pixel and its neighbors. Directional local extrema patterns explore the directional information in 0°, 90°, 45° and 135° for a pixel positioned at the center. A mesh structure is created from a quantized extrema to derive significant textural information. Initially, the directional quantized data from the mesh structure is extracted to form LMeQEP of given image. Then, RGB color histogram is built and integrated with the LMeQEP to enhance the performance of the system. In order to test the impact of proposed method, experimentation is done with bench mark image repositories such as MIT VisTex and Corel-1k. Avg. retrieval rate and avg. retrieval precision are considered as the evaluation metrics to record the performance level. The results from experiments show a considerable improvement when compared to other recent techniques in the image retrieval.

Image Holistic Scene Understanding Based on Image Intrinsic Characteristics and Conditional Random Fields

Image holistic scene understanding based on image intrinsic characteristics and conditional random fields is proposed. The model integrates image scene classification, image semantic segmentation and object detection. 1) For the scene classification, we use method of PHOW feature extraction plus KPCA dimensional reduction to obtain feature information for each image. 2) For object detection section, saliency detection and segmentation characteristics of the image object detection is useful. We propose the method by integrating image segmentation information got by the method proposed in literature [1]. 3) For the semantic segmentation: (1) For the unary potentials, we incorporating HOG, RGB color histogram and LBP features by the methods proposed in literature [2]; (2) The image manifold structural features can better reflect the importance between hyper-pixel regions and eventually boost accuracy. Therefore, we add the higher-order potential item to reflect inherent manifold image feature of each super pixel region. The experiments testify that model performance has raised on all three sub-tasks.

Local quantized extrema patterns for content-based natural and texture image retrieval

AbstractThis paper proposes a novel feature descriptor, named local quantized extrema patterns (LQEP) for content based image retrieval. The standard local quantized patterns (LQP) collect the directional relationship between the center pixel and its surrounding neighbors and the directional local extrema patterns (DLEP) collect the directional information based on local extrema in 0°, 45°, 90°, and 135° directions for a given center pixel in an image. In this paper, the concepts of LQP and DLEP are integrated to propose the LQEP for image retrieval application. First, the directional quantized information is collected from the given image. Then, the directional extrema is collected from the quantized information. Finally, the RGB color histogram is integrated with the LQEP for a feature vector generation. The performance of the proposed method is tested by conducting three experiments on Coel-1K, Corel-5K and MIT VisTex databases for natural and texture image retrieval. The performance of the proposed method is evaluated in terms of precision, recall, average retrieval precision and average retrieval rate on benchmark databases. The results after investigation show a considerable improvements in terms of their evaluation measures as compared to the existing methods on respective databases.

The Improvement of Mean-Shift Algorithm in Target Tracking

The bandwidth of kernel function is invariance in Mean-Shift tracking process which led to the problem of actual process of tracking failure. To solve this question a method is proposed to combine object contour, RGB color histogram and Mean-Shift tracking algorithm. In each frame the contour and color feature of target object are extracted to as a model. Meanwhile the size of contour is used as the bandwidth for kernel function in the next frame. Based on this Method the accuracy of Mean-Shift tracking is improved and the error of Mean-Shift tracking is reduced.

IJARCCE - Computer and Communication Engineering

Content Based Image Retrieval is a challenging method of capturing relevant images from a large storage space. A new low level feature contains histogram, color and texture information. This element is intended for use in image retrieval and image indexing systems. This paper experiments various methods available for Content based image retrieval System, they are RGB Color Histogram, Tamura Texture and Gabor Feature. The methods are implemented and tested based on three parameters like Precision value, Recall value and Accuracy rate. The Experimental results show that Gabor Feature method is more efficient when comparing with other methods. The Gabor Feature 81.7%Accuracy in Content Based Image Retrieval system.

Color and Rotated M-Band Dual Tree Complex Wavelet Transform Features for Image Retrieval

In this paper, a novel algorithm which integrates the RGB color histogram and texture features for content based image retrieval. A new set of two- dimensional (2-D) M-band dual tree complex wavelet transform (M_band_DT_CWT) and rotated M_band_DT_CWT are designed to improve the texture retrieval performance. Unlike the standard dual tree complex wavelet transform (DT_CWT), which gives a logarithmic frequency resolution, the M-band decomposition gives a mixture of a logarithmic and linear frequency resolution. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for image retrieval using M_band_DT_CWT and rotated M_band_DT_CWT (M_band_DT_RCWT) by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval performance, two texture databases are used. Further, it is mentioned that the databases used are Brodatz gray scale database and MIT VisTex Color database. The retrieval efficiency and accuracy using proposed features is found to be superior to other existing methods. Index Terms—Color; M-band wavelets; Feature Extraction; M-band dual tree complex wavelets; Image Retrieval.

Application of Digital Image Processing Software in Ceramic Design

From rest to exercise and from two-dimensional to three-dimensional, digital image processing software can be multi-angle, multi-dimension, multi-direction infiltration for the various fields of design, which has wide applications in every corner of digital media. Firstly, the digital image processing software can analyze the common digital processing system model. On the basis of the principle of RGB color histogram, it also can analyze its application in the ceramic design. According to the set of image transformation principle, the analysis of the geometric transformation plays an important role in in ceramic set, to provide the theoretical basis for the digital image processing software to a certain extent.

Video Browsing Service for U-health Care Systems Environments

Recently, Digital video is one of the important information media delivered on the Internet and playing an increasingly important role in multimedia. This paper proposes a Video Browsing Service(VBS) that provides both the video content retrieval and the video browsing by the real-time user interface on Web. For the scene segmentation and key frame extraction of video sequence, we proposes an efficient scene change detection method that combines the RGB color histogram with the χ 2 (Chi Square) histogram. Resulting key frames are linked by both physical and logical indexing. This system involves the video editing and retrieval function of a VCR's. Three elements that are the date, the field and the subject are used for video browsing. A Video Browsing Service is implemented with MySQL, PHP and JMF under Apache Web Server.

Adaptive key frame extraction for video summarization using an aggregation mechanism

Video summarization is a method to reduce redundancy and generate succinct representation of the video data. One of the mechanisms to generate video summaries is to extract key frames which represent the most important content of the video. In this paper, a new technique for key frame extraction is presented. The scheme uses an aggregation mechanism to combine the visual features extracted from the correlation of RGB color channels, color histogram, and moments of inertia to extract key frames from the video. An adaptive formula is then used to combine the results of the current iteration with those from the previous. The use of the adaptive formula generates a smooth output function and also reduces redundancy. The results are compared to some of the other techniques based on objective criteria. The experimental results show that the proposed technique generates summaries that are closer to the summaries created by humans.

Evaluation of Mammary Cancer in 7,12-Dimethylbenz(a)anthracene-Induced Wister Rats by Asymmetrical Temperature Distribution Analysis Using Thermography: A Comparison with Serum CEA Levels and Histopathology

Animal surface temperature profile captured using infrared camera is helpful for the assessment of physiological responses associated with the regulation of body temperature. Diagnosing breast cancer in early stage itself has a greater effect on the prognosis. In this work, asymmetrical temperature distribution analysis of chemical carcinogen 7,12-dimethyl benz(a)anthracene-induced in the lower right flank region of Wistar rats (n = 6) was carried out to test the potential of thermography in diagnosing mammary cancer and tumor growth over a period of nine weeks in comparison with histopathology results as standard. Temperature difference between the tumor induced lower right and left side of flank region was significant (with P value <0.001), whereas in the abdomen and shoulder there was no significant difference in temperature between right and left sides. Percentage of asymmetrical temperature difference in the tumor induced lower flank region was 0.5 to 2%, whereas in the other regions it was <0.5%. Green pixel distribution in RGB color histogram was asymmetrical in the tumor induced lower flank region. Temperature reduction was observed in the tumor induced region after the seventh day of carcinogen induction. Asymmetrical thermogram analysis is the best method of diagnosing mammary cancer and for studying tumor development.