HSV Color Histogram Research Articles

Identification of Fresh and Unfresh Fish Based on Eye Image Using The Self-Organizing Maps (SOM) Method

Fish is a common protein source and easy to obtain in Indonesia, but because of its high water content, fish quickly spoils. Fish freshness can be detected using several conventional methods, such as chemical analysis, biochemistry, microbiological analysis, and sensory examination. Another identification method involves observing the color of the fish's eyes. Fish identification is crucial before any further processing, ensuring that the fish's quality delivered to consumers remains high. To tackle the problem of differentiating between fresh and non-fresh fish, this research employs Self-Organizing Maps (SOM) as the primary methodology. This research focuses on identifying fresh and non-fresh fish using the SOM method, using actual data involving tilapia as the research object. The data includes eye images of new and non-fresh fish, and various procedures are required to obtain the desired data. This data is then used as training and testing data. The process continues with the preprocessing stage, which is a data modification process to improve performance in subsequent steps and feature extraction using HSV color histograms. Classification of processed data is carried out using the SOM method. Once completed, the identification results are displayed. This research produces a system for identifying fresh and non-fresh fish based on eye images using SOM, which achieves a good accuracy of 85.71%.

Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. YOLOv3 pretraining model is used for model training with sample images for ship detection. The ship detection model is built by adjusting and optimizing parameters. Combining the target HSV color histogram features and LBP local features’ target, object recognition and selection are realized by using the deep learning model due to its efficiency in extracting object characteristics. Since tracking targets are subject to drift and jitter, a self-correction network that composites both direction judgment based on regression and target counting method with variable time windows is designed, which better realizes automatic detection, tracking, and self-correction of moving object numbers in water. The method in this paper shows stability and robustness, applicable to the automatic analysis of waterway videos and statistics extraction.

Content-based image retrieval using hybrid k-means moth flame optimization algorithm

Content-based image retrieval plays a key role in many domains and the volume of the image databases increases tremendously; it is very difficult to compare query image feature with every image in the dataset during the retrieval phase. Hence, search space and computational complexity increase which degrades the performance of recognition accuracy. This system investigates various search space reduction techniques, which partition or classify the image collection into a subset of related images. This study proposes an image clustering using the hybrid K-means moth flame optimization algorithm (KMFO). It enhances the performance of the K-means algorithm by assigning the optimum number of clusters and cluster centroids using the number of flames and flame values obtained in MFO. It uses color moments, HSV color histogram, color correlogram, GLCM, wavelet transform, dominant color, and region-based descriptors as feature vectors. The experiments are tested on Corel 1K dataset, and it shows competent results when compared with other retrieval techniques.

Application of content based image retrieval in digital image search system

Multimedia data is growing rapidly in the current digital era, one of which is digital image data. The increasing need for a large number of digital image datasets makes the constraints faced eventually drain a lot of time and cause the process of image description to be inconsistent. Therefore, a method is needed in processing the data, especially in searching digital image data in large image dataset to find image data that are relevant to the query image. One of the proposed methods for searching information based on image content is content based image retrieval (CBIR). The main advantage of the CBIR method is automatic retrieval process, compared to traditional keyword. This research was conducted on a combination of the HSV color histogram methods and the discrete wavelet transform to extract color features and textures features, while the chi-square distance technique was used to compare the test images with images into a database. The results have showed that the digital image search system with color and texture features have a precision value of 37.5% - 100%, with an average precision value of 80.71%, while the percentage accuracy is 93.7% - 100% with an average accuracy is 98.03%.

A survey on the integrated analysis of HSV color histogram characteristics with shape-based edge detection to improve the retrieval system performance in content-based image retrievals

A survey on the integrated analysis of HSV color histogram characteristics with shape-based edge detection to improve the retrieval system performance in content-based image retrievals

Hybrid Saliency-SVM Method Implementation for Automatic Data Training Selection in Image Segmentation

Image segmentation is one of the most important step in computer vision and image processing, which later will be used in image retrieval, object identifying and data classification. Image segmentation can be seen as classification problem, namely by marking each pixels according to certain characteristics. Support Vector Machine (SVM) is a classification method included in supervised learning. Supervised learning is a method which requires training and testing. Training sample used in training process isn’t always exist in few cases, especially in the image segmentation case. This particular research implemented SVM-based method which is Saliency-SVM for automatic data training selection in image segmentation. This method generates data training using SVM-based visual saliency detection where there is pre-segmentation step and trimap formation based on saliency information from visual saliency detection, HSV color space quantitation, histogram analysis and local homogeneity threshold. Data training produced is pixel belonging to positive (object) and negative (background). The step before segmentation done with SVM is feature extraction to create input vector in SVM. Object segmentation on the image is done by SVM based on SVM Trained Model. Test result from Saliency-SVM for this image segmentation has average accuracy value up to 94,84 % compared to the image ground truth

Content Based Image Retrieval and Support Vector Machine Methods for Face Recognition

The development of biometrics is growing rapidly. The recognition as non-trivial element in biometrics is not only using fingerprints, but also human face. The purpose of this research is to implement both Content Based Image Retrieval (CBIR) and Support Vector Machine (SVM) methods in the face recognition system with a combination of features extraction. CBIR method interprets images by exploiting several features. The feature usually consists of texture, color, and shape. This research utilizes color, texture, shape and shape coordinate features of the image. The proposed algorithms are HSV Color Histogram, Color Level Co-Occurrence Matrix (CLCM), Eccentricity, Metric, and Hierarchical Centroid. SVM method is used to train and classify the extracted vectors. The result shows that the proposed system is 95% accurate in recognizing faces with different resolutions.

Розробка пристрою для автоматичного фенотипування насіннєвого матеріалу соняшнику

Seed phenotyping is the process of typing, determining, identifying and dividing seeds, as a breeding material, according to its morphological and marker features. Technical and technological support of the process of automatic phenotyping of seeds is based on algorithms and methods for processing images of seeds. The aim of the research is to increase the efficiency of the sunflower breeding and seed process by developing and using a device for automatic phenotyping of seed material. An algorithm for identifying and calculating the geometric dimensions and color of seeds, on which the device for automatic phenotyping of sunflower seed is based, has been developed and experimentally verified. The specified algorithm is implemented in software that uses OpenCV libraries. The software allows identification of sunflower seeds by its geometric dimensions (length L, width B and their ratio) in the HSV color space and histograms of the distribution of colors of the RGB region of the seed. A device has been developed for automatic phenotyping of seeds, which preserves the accuracy of individual measurement of the geometric dimensions of sunflower seeds, determining their shape and color corresponding to modern measuring tools, and provides low labor intensity and high manufacturability of the implementation of the procedure of phenotyping seeds as a selection material, according to its morphological and marker features.

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.

Compressed sensing image restoration algorithm based on improved SURF operator

Abstract Aiming at the excellent descriptive ability of SURF operator for local features of images, except for the shortcoming of global feature description ability, a compressed sensing image restoration algorithm based on improved SURF operator is proposed. The SURF feature vector set of the image is extracted, and the vector set data is reduced into a single high-dimensional feature vector by using a histogram algorithm, and then the image HSV color histogram is extracted.MSA image decomposition algorithm is used to obtain sparse representation of image feature vectors. Total variation curvature diffusion method and Bayesian weighting method perform image restoration for data smoothing feature and local similarity feature of texture part respectively. A compressed sensing image restoration model is obtained by using Schatten-p norm, and image color supplement is performed on the model. The compressed sensing image is iteratively solved by alternating optimization method, and the compressed sensing image is restored. The experimental results show that the proposed algorithm has good restoration performance, and the restored image has finer edge and texture structure and better visual effect.

Early Detection of High Blood Pressure and Diabetic Retinopathy on Retinal Fundus Images Using CBRIR Based on Lifting Wavelets

In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.

HSIC-based kernel independent component analysis for fault monitoring

For nonlinear and non-Gaussian industrial processes, KICA is a mature and successful method for fault monitoring. However, a fixed-point ICA algorithm uses a negative entropy method, which has relatively high requirements for non-quadratic function and initial point; accuracy is unsatisfactory. The calculation of control limits of Hotelling's T2 statistic is based on a kernel density estimation, which is difficult to calculate and implement and sometimes cannot guarantee accuracy. In this paper, the kernel independent component analysis method based on the Hibert-Schmidt independence criterion (HSIC) is used instead of the fixed-point ICA algorithm for fault monitoring to improve the accuracy of the independent element. We obtain the independent element directly from the objective function, rather than through the combination of KPCA and ICA. At the same time, we use the direct binomial expansion theorem to obtain the control limit, which reduces computational complexity and implementation difficulty and improves accuracy. The control limit is improved to obtain multi-fault diagnosis. Gray-level information and color information of each frame of the video are respectively read through the information entropy and HSV spatial color histogram. Experimental results show the advantage and effectiveness of the proposed approach. Meanwhile, shadow variables are introduced to smooth the statistics. The contributions of this paper are as follows. 1) Using the kernel independent component method based on HSIC for fault monitoring improves speed and accuracy. 2) The binomial expansion theorem is used instead of traditional kernel density estimation to calculate the control limit, which improves the results of fault monitoring. 3) A method of fault detection using information entropy, HSV color histogram and multivariate statistical analysis is presented.

Wakes-Ship Removal on High-Resolution Optical Images based on Histograms in HSV Color Space

Ship detection on optical remote sensing images is getting great attention; however, some images called wakes-ship have not been taken into account yet. Current works in ship detection are focusing on in-shore detection where ships are in calm; furthermore, their methods get high Intersection Over Union (IoU), above 70%, but when computing IoU using only wakes-ship images the ratio is 22%. In this paper, it is presented a new framework to improve ship segmentation on wakes-ship images. In order to achieve this goal, it was considered HSV color space and histograms. First, ship detection was done using state-of-the-art ship detection methods. Second, bin histograms in HSV color space was used to get the colors that rely on wakes. Finally, the removal of wakes from ships was done using some discriminative properties. In this way, the increase of the IoU performance at wake-ship segmentation goes from 22% to 63%, which is an improvement of 186%.

Robust object tracking based on adaptive templates matching via the fusion of multiple features

Moving object tracking under complex scenes remains to be a challenging problem because the appearance of a target object can be drastically changed due to several factors, such as occlusions, illumination, pose, scale change and deformation. This study proposes an adaptive multi–feature fusion strategy, in which the target appearance is modeled based on timed motion history image with HSV color histogram features and edge orientation histogram features. The variances based on the similarities between the candidate patches and the target templates are used for adaptively adjusting the weight of each feature. Double templates matching, including online and offline template matching, is adopted to locate the target object in the next frame. Experimental evaluations on challenging sequences demonstrate the accuracy and robustness of the proposed algorithm in comparison with several state-of-the-art algorithms.

Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features

This research paper is an attempt to present Content Based Image Retrieval (CBIR) system developed for retrieving diseased leaves of soybean. It uses color, shape and texture features of leaf. Color features are extracted using HSV color histogram. Scale Invariant Feature Transform (SIFT) provides shape features in the form of matching key points. Local Binary Pattern (LBP) and Gabor filter are widely used texture features. Novel texture feature named Local Gray Gabor Pattern (LGGP) is proposed by combining LBP and Gabor. Performance of all these features with respect to retrieval precision is tested for three soybean leaf diseases. Further color, shape and texture features are combined to increase performance. It is found that when LGGP is combined with color histogram and SIFT retrieval precision is improved. Retrieval efficiency of about 96%, 68% and 76% is achieved for soybean leaves affected by mosaic virus, septoria brown spot and pod mottle disease respectively. Average retrieval efficiency of 80% (for the top 5 retrieval) and 72% (for the top 10 retrieval) is obtained by combined features. This retrieval precision is database dependent and varies with size of the database and quality of images.

Robust visual tracking based on hierarchical appearance model

In order to track the target object effectively in the presence of significant appearance variation, e.g., occlusion, scale variation, deformation, fast motion and background clutter, we develop a new approach based on hierarchical appearance model under the Bayesian framework. The proposed approach represents the target at two levels, i.e., the local and the global levels. At the local level, a set of local patches are used to represent the target so as to adapt the changes in appearance. Likelihood defined as the weighted sum of reliability index and stability index is applied to evaluate how likely a patch pertaining to the target. At the global level, the target is represented by using double bounding boxes regarding the foreground and background, respectively. The inner bounding box only contains the target region, and the outer bounding box contains both the target region and the background region surrounding the target. The target model is encoded by using two HSV color histograms with respect to the target and the background, respectively. As this, the drifts can be effectively suppressed in the tracking process. Furthermore, the object position can be estimated by maximizing the likelihood of the target under the Bayesian framework. An experimental study is employed to illustrate the advantages of our proposed approach. The experimental results demonstrate that our method is very effective and performs favorably in comparison to the state-of-the-art trackers in terms of efficiency, accuracy and robustness.

Image Retrieval using Multi Cascaded Features

This paper depends on Content Based Image Retrieval CBIR to retrieve desired images from a large images database. It is based on extracting cascade features from image such as color, shape and texture features. The Euclidean Distance is used to measure similarity of each feature and retrieve similar images from the image database. The proposed method contains three stages: in the first stage, the HSV color histogram from query image is found and the nearest 100 images are retrieved. In the second stage the shape features are extracted from edge detection and the nearest 50 image are retrieved. In the last stage the texture feature are found based on first order features and the nearest 10 images are retrieved. In this paper a mechanism for image retrieval based on cascading approach is developed to improve image retrieval performance and reduce the computational time required to retrieve images. It is found that cascading features with Euclidean distance give about 76% precession of image retrieval.

English

English

Color Histogram and DBC Co-Occurrence Matrix for Content Based Image Retrieval

This paper presents the integration of color histogram and DBC co-occurrence matrix for content based image retrieval. The exit DBC collect the directional edges which are calculated by applying the first-order derivatives in 0o , 45o , 90o and 135o directions. The feature vector length of DBC for a particular direction is 512 which are more for image retrieval. To avoid this problem, we collect the directional edges by excluding the center pixel and further applied the rotation invariant property. Further, we calculated the co- occurrence matrix to form the feature vector. Finally, the HSV color histogram and the DBC co-occurrence matrix are integrated to form the feature database. The retrieval results of the proposed method have been tested by conducting three experiments on Brodatz, MIT VisTex texture databases and Corel-1000 natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, DBC and other transform domain features.

Real-Time Tracking by Double Templates Matching Based on Timed Motion History Image with HSV Feature

It is a challenge to represent the target appearance model for moving object tracking under complex environment. This study presents a novel method with appearance model described by double templates based on timed motion history image with HSV color histogram feature (tMHI-HSV). The main components include offline template and online template initialization, tMHI-HSV-based candidate patches feature histograms calculation, double templates matching (DTM) for object location, and templates updating. Firstly, we initialize the target object region and calculate its HSV color histogram feature as offline template and online template. Secondly, the tMHI-HSV is used to segment the motion region and calculate these candidate object patches' color histograms to represent their appearance models. Finally, we utilize the DTM method to trace the target and update the offline template and online template real-timely. The experimental results show that the proposed method can efficiently handle the scale variation and pose change of the rigid and nonrigid objects, even in illumination change and occlusion visual environment.