Dominant Color Descriptor Research Articles

Sistem Temu Kembali Citra Termal Kanker Payudara Pada Citra Rekam Medis

Medical record images (Medical Imaging) in the form of the thermal images can be able to detect of of breast cancer as early detector for the patient. The technic process is by utilizing the thermal image of an existing breast cancer patient, the thermal image of the new patient can be searched by the similarities the image of the old patient. In order to get the exactly results, required an accurate system to find the thermal imaging of breast cancer patients in the thermal image database. This study aims to build a Content Based Imaged Retrieval (CBIR) system for thermal images of breast cancer patients based on thermal images have been diagnosed by specialist doctors. This system with using a combination of color histogram features and dominant color descriptors. To determine the similarity between the query image and the dataset image with using of two methods that is measuring the Euclidean Distance and the Minkowski Distance. The results of this research show from testing the combination between the two features, the F-measure evaluation value obtained from the top 10 retrieval was in the healthy image category of 0.07 and 0.09 in the cancer image category. From the conducting testing process result, the concluded is the most appropriate feature in carrying out the breast thermal image retrieval process is using a combination of dominant color descriptor features and color histograms.

A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease

Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy.

New Dominant Color Descriptor Features Based on Weighting of More Informative Pixels using Suitable Masks for Content-Based Image Retrieval

Content-based image retrieval (CBIR) is a process of retrieving images based on their content in a dataset automatically. CBIR is a common solution to search images similar to a desired image among all images in dataset. To do this, many methods have been developed to extract images features. Here, a new Dominant Color Descriptor (DCD) method is proposed to improve CBIR accuracy. In the first step, Canny edges of images are extracted. In the next step, edges are widened by employing morphological operations. Finally, pixels that are not at the edges are weighted less than the pixels which are located at edges. Indeed, pixels in regions with low color variations are less weighted and more informative pixels are more weighted in providing DCD features. To show the effectiveness of the proposed method, experiments are performed on three datasets Corel-1k, Corel-10k and Caltech256. Results demonstrate that the proposed method outperforms competitive methods.

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.

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.

A New Content-Based Image Retrieval System Using Deep Visual Features

Content-based image retrieval (CBIR) is a technique for images retrieval based on their visual features, i.e. induced by their pixels. The images are, classically, described by the image feature vectors. Those vectors reflect the texture, color or a combination of them. The accuracy of the CBIR system is highly influenced by the (i) definition of the image feature vector describing the image, (ii) indexing and (iii) retrieval process. In this paper, we propose a new CBIR system entitled ISE (Image Search Engine). Our ISE system defines the optimum combination of color and texture features as an image feature vector, including the Particle Swarm Optimization (PSO) algorithm and employing an Interactive Genetic Approach (GA) for the indexing process. The performance analysis shows that our suggested PCM (Proposed Combination Method) upgrades the average precision metric from 66.6% to 89.30% for the “Food” category color histogram, from 77.7% to 100% concerning CCVs (Color Coherence Vectors) for the “Flower” category and from 58% to 87.65% regarding the DCD (Dominant Color Descriptor) for the “Building” category using the Corel dataset. Besides, our ISE system showcases an average precision of 98.23%, which is significantly higher than other CBIR systems presented in related works.

Structure preservation in content‐aware image retargeting using multi‐operator

The evolution of image retargeting technique demands the exploitation of multi-operators since they are capable of preserving the structure and salient objects of the image. However, these multi-operators are mostly based on seam carving with scaling or cropping operators which lead to significant distortions in the retargeted image. This study proposes a new multi-operator scheme which has improved seam carving, through the proposed seam diversion based image retargeting algorithm, integrated with the cropping and warping operator. A total of six different multi-operator schemes have been proposed out of which the MO6 technique gave remarkable results in terms of image quality, least distortion, and lowest run-time. To simplify image retargeting operations, an optimised image distance function was used. The optimised image distance function was formulated which combines bidirectional image Euclidean distance, dominant colour descriptor, and an energy-based coefficient to bypass seams from the point where seams start clashing and hence the defined threshold violates. By integrating cropping and warping into the proposed algorithm, it preserves the salient features of the retargeted images. Typical results have been presented which demonstrates the effectiveness of the proposed methods. User-based subjective analysis has also been carried out which shows that image retargeted using the MO6 technique has high user preference.

Combination of Dominant Color Descriptor and Hu Moments in Consistent Zone for Content Based Image Retrieval

The rapidly increasing number of digital images requires effective retrieval. Meanwhile, the dominant color descriptor has been widely used in image processing. Due to the influence of lighting and other factors, the same color in nature may have some different changes. The human eye is usually more sensitive to zones of consistent color, often identifying objects by zones of consistency. Therefore, the proposed method in this paper first applies the texton template to detect and extract the consistent zone of an image, and calculates the dominant color descriptor feature on the pixels in this consistent zone. Besides, the translation and rotation invariance of the Hu moments feature is applied to extract the shape information in the same consistent zone of the image. Finally, the combination of the dominant dolor descriptor and the Hu moments is used for content-based image retrieval. The algorithm proposed in this paper is tested on three data sets: Corel-1k, Corel-5k and Corel-10k, and the experimental results show that it is superior to the current content-based image retrieval methods.

Classification of yield affecting biotic and abiotic paddy crop stresses using field images

On-time recognition and early control of the stresses in the paddy crops at the booting growth stage is the key to prevent qualitative and quantitative loss of agricultural yield. The conventional paddy crop stress identification and classification activities invariably rely on human experts to identify visual symptoms as a means of categorization. This process is admittedly subjective and error-prone, which in turn may lead to incorrect action in stress management decisions. The proposed work presented in this paper aims to develop an automated computer vision system for the recognition and classification of paddy crop stress types from the field images using the state-of-the-art color features. The work examines the impact of eleven stress types, two biotic and nine abiotic stresses, on five different paddy crop varieties during the booting growth stage using field images and analyzes the stress responses in terms of color variations using lower-order color moments and two visual color descriptors defined by the MPEG-7 standard, the Dominant Color Descriptor (DCD) and Color Layout Descriptor (CLD). The Sequential Forward Floating Selection (SFFS) algorithm has been employed to reduce the overlapping between the features. Three different classifiers, the Back Propagation Neural Network (BPNN), the Support Vector Machine (SVM), and the k-Nearest Neighbor (k-NN) have been deployed to distinguish among stress types. The average stress classification accuracies of 89.12%, 84.44% and 76.34% have been achieved using the BPNN, SVM, and k-NN classifiers, respectively. The proposed work finds application in the development of decision support systems and mobile apps for the automation of crop and resource management practices in the field of agricultural science.

Identifikasi Tingkat Kematangan Kelapa Sawit Berbasis Pencitraan Termal

Indonesia is the biggest producer of palm oil (Elaeis guineenis jacq). The palm tree is a primary commodity that posses a high economic value. Palm oil must be considered in terms of quality to produce optimal and high-quality oil. Previously, the stipulation of the palm tree characterization used manual and visual image utilization method; it may have weaknesses due to the dependency of individual sorting and coruscation factor. Therefore, this research is aimed to improve the performance of the previous method in identifying the ripeness of palm tree based on thermal imaging. The excess of thermal imaging was not related to the coruscation since the level of ripeness was both determined by the temperature and colour. The detection method of this research deployed the colour-based features that are Dominant Colour Descriptor and Color Moment. The DCD and Color Moment was the input to the K-Nearest Neighbor (KNN) method. The percentage of identification rate was 89%, and the identification of oil palm maturity level using thermal imaging is more efficient because it is done without human intervention and does not depend on lighting assistance compared to manual method and method of using RGB visual images.

An efficient seed points selection approach in dominant color descriptors (DCD)

The content-based image retrieval (CBIR) system accepts the input in the form of images and retrieves the relevant images from the database. The CBIR system automatically extracts the prominent key information from the image involved in the retrieval task. The color is one of the key information of the image and it is represented by dominant color descriptors (DCD). Here, similar colors get clustered and the mean value of each cluster represents the dominant color. The random number of unstable cluster formation in DCD alleviates the CBIR system performance. The proposed work has minimized the drawback of DCD by introducing seed points selection based on the mean, maximum and minimum value of the color pixels present in the image. Moreover, this work suggests the optimal cluster number by validating the different combinations of the proposed stable dominant color clusters. The retrieval precision of the proposed CBIR has improved since this work gives equal weight for both the dominant color and its occurrence probability in distance metric calculation. Finally, four standard datasets namely Wang’s, Corel-10k, OT-scene, and Oxford flower are considered for evaluation, and it gives more number of relevant images compared to the state-of-the-art dominant color feature extraction techniques used on these datasets.

Optimized Feature Integration and Minimized Search Space in Content Based Image Retrieval

Abstract The semantic gap between the user request and retrieval result is an important but unsolved problem in the content-based image retrieval (CBIR) systems. This paper introduces a new multi-level structure in a CBIR system to bridge the semantic gap using the combination of low-level visual contents of an image. The initial stage of the proposed system depends on the statistical information of the color images which gives the most prominent images for the further level of the process. In the next step, low-level features such as color and texture details are extracted using dominant color descriptor (DCD) and radial mean local binary pattern over the query and selected images. Subsequently, Particle Swarm Optimization (PSO) is applied over both the color and texture similarity measure between the query and selected images. Finally, this multi-level system is experimented on OT-scene and Corel-10k databases to assess the performance and it gives 78.43% and 52.34% average precision rate.

A Novel Method For CBIR Using ACO-SVM With DTCWT And Color Features

Content Based Image Retrieval (CBIR) has been the major thing for looking associated content material on the net in the form of pictures, knowledge and so on. A retrieval approach which organization color and texture feature is future in this paper. In this research, implement a new technique for CBIR by ant colony optimization (ACO) and Support vector machine (SVM) with dual tree complex wavelet transform (DTCWT) and color features.In this work, has been used color or texture features for CBIR. In the increase of feature extraction, firstly extract texture feature using DTCWT for resolving the problematic of redundant CWT. After that, extract color feature using dominant color descriptor (DCD) on RGB and HSV color space for improving computation and efficiency. The experimental dataset contain 1000 images including horses, elephants, food, African people, etc. The equal dimension is calculated using weighted Euclidean distance (WED). For improving effectiveness of the system, used ACO for the greatest result and classify data using SVM. The performance analysis is based on precision and F-measure.

An efficient image retrieval system with structured query based feature selection and filtering initial level relevant images using range query

Content Based Image Retrieval is a proficient way of storing, managing, indexing, searching, browsing, mining or retrieving images from a large image repository. Most of the researchers are intensely competing for developing an efficient and precise image retrieval system with less time and space constraint. The proposed method creates two different techniques to reduce the space and time constraints. The first method develops an efficient CBIR system by reducing a number of features to obtain an optimal feature subset using SQL query based feature selection for the normalized feature set. The second method uses SQL range query to filter out initial level relevant images and further the Euclidean distance is applied to refine the filtered subspace inorder to obtain the most relevant images. Gray-level co-occurrence matrix, Region based image descriptors and dominant color descriptor are used to extract the features. Elapsed time, retrieval precision and recall are the evaluation metrics used to analyze the performance with other image retrieval systems. The experiment was performed on Corel dataset and it shows superior performance over the previous systems.

An effective fusion model for image retrieval

In the past decade, the popular Bag of Visual Words approach has been applied to many computer vision tasks, including image classification, video search, robot localization, and texture recognition. Unfortunately, most approaches use intensity features and discard color information, an important characteristic of any image that is motivated by human vision. Besides, if background colors are higher than foreground ones, Dominant Color Descriptor (DCD) retrieves images that contain similar background colors correctly. On the other hand, just color feature extraction is not sufficient for similar objects with different color descriptors (e.g. white dog vs. black dog). To solve these problems, a new Salient DCD (SDCD) color descriptor is proposed to extract foreground color and add semantic information into DCD based on the color distances and salient object extraction methods. Besides, a new fusion model is presented to fuse SDCD histogram and PHOW MSDSIFT histogram. Performance evaluation on several datasets proves that the new approach outperforms other existing, state-of-the-art methods.

Effectiveness of MPEG-7 Color Features in Clothing Retrieval

Clothing is a human used to cover the body. Clothing consist of dress, pants, skirts, and others. Clothing usually consists of various colors or a combination of several colors. Colors become one of the important reference used by humans in determining or looking for clothing according to their wishes. Color is one of the features that fit the human vision. Content Based Image Retrieval (CBIR) is a technique in Image Retrieval that give index to an image based on the characteristics contained in image such as color, shape, and texture. CBIR can make it easier to find something because it helps the grouping process on image based on its characteristic. In this case CBIR is used for the searching process of Muslim fashion based on the color features. The color used in this research is the color descriptor MPEG-7 which is Scalable Color Descriptor (SCD) and Dominant Color Descriptor (DCD). The SCD color feature displays the overall color proportion of the image, while the DCD displays the most dominant color in the image. For each image of Muslim women's clothing, the extraction process utilize SCD and DCD. This study used 150 images of Muslim women's clothing as a dataset consistingclass of red, blue, yellow, green and brown. Each class consists of 30 images. The similarity between the image features is measured using the eucludian distance. This study used human perception in viewing the color of clothing.The effectiveness is calculated for the color features of SCD and DCD adjusted to the human subjective similarity. Based on the simulation of effectiveness DCD result system gives higher value than SCD.

Face Detection Using Gabor Wavelet Transform with Histogram of Local Binary Pattern with Dominant Color Descriptor

Face Detection Using Gabor Wavelet Transform with Histogram of Local Binary Pattern with Dominant Color Descriptor

New content‐based image retrieval system based on optimised integration of DCD, wavelet and curvelet features

A new content-based image retrieval (CBIR) scheme is proposed based on the optimised combination of the colour and texture features to enhance the image retrieval precision. This work focuses on a uniform partitioning scheme which is applied in the Hue, Saturation and Value (HSV) colour space to extract dominant colour descriptor (DCD) features. In the proposed CBIR scheme, the DCD features are initially extracted as the colour features, and then an appropriate similarity measure is applied. Also, several wavelet and curvelet features are defined as texture features to overcome the noise and the problem of image translation. Finally, the colour and texture features are optimally combined by using the particle swarm optimisation algorithm. The findings show that not only the proposed colour, wavelet and curvelet features outperform the existing ones but also their optimum combination has a better accuracy in comparison with several contemporary CBIR systems. The performance analysis shows that the proposed method improves the average precision metric from 67.85 to 71.05% for DCD, 58.90 to 65.43% for wavelet and 53.18 to 56.00% for curvelet using Corel dataset. In addition, the optimum combination presents the average precision of %76.50 which is significantly higher than the other state-of-the-art methods.

Penerapan Deskriptor Warna Dominan untuk Temu Kembali Citra Busana pada Peranti Bergerak

Nowadays, clothes with various designs and color combinations are available for purchasing through an online shop, which is mostly equipped with keyword-based item retrieval. Here, the object in the online database is retrieved based on the keyword inputted by the potential buyers. The keyword-based search may bring potential customers on difficulties to describe the clothes they want to buy. This paper presents a new searching approach, using an image instead of text, as the query into an online shop. This method is known as content-based image retrieval (CBIR). Particularly, we focused on using color as the feature in our Muslimah clothes image retrieval. The dominant color descriptor (DCD) extracts the wardrobe's color. Then, image matching is accomplished by calculating the Euclidean distance between the query and image in the database, and the last step is to evaluate the performance of the DWD by calculating precision and recall. To determine the performance of the DCD in extracting color features, the DCD is compared with another color descriptor, that is dominant color correlogram descriptor (DCCD). The values of precision and recall of DCD ranged from 0.7 to 0.9 while the precision and recall of DCCD ranged from 0.7 to 0.8. These results showed that the DCD produce a superior performance compared to DCCD in retrieving a set of clothing image, either plain or patterned colored clothes.

Analysis of Color Standardization Methods for the Automatic Quantification of IHQ Stain in Breast TMA

Introduction/ Background IHC biomarkers in breast TMA samples are used daily in pathology departments. This generates large amounts of information, which requires careful analysis [1]. Automatic methods to evaluate positive staining have been investigated since they may save time and reduce errors in the diagnosis that are due to subjective evaluation. Aims The aim of this work is to develop a density tool able to automatically quantify the positive brown IHC stain in breast TMA. One of the problems when dealing with stained samples is color variation and distortion [2]. This is due to several factors such as the fixation process, the amount of stain or the digitalization process. One solution to the color variation problem is to apply standardization of reagents and procedures in histological practice. However, stains fade over time and therefore, it is not possible to achieve complete standardization with the current technology. In this paper, different methods for stain normalization have been analyzed and compared in density quantification. Methods The methods implemented for stain normalization are based on the use of color distribution by means of dominant color descriptor, scalable and color structure descriptor. These algorithms adjust the color values of an image on a pixel-by-pixel basis so as to match the color distribution of the source image to that of a target image. Then, two main processes were performed to estimate TMA density: a) evaluation of total cylinder area and b) quantification of IHC stained area. For the 1st process, the algorithm distinguishes between normal, broken or distorted cylinders. The 2nd process deals with the evaluation of the positive brown pixels inside the cylinder. The segmentation is based on Lab thersholding together with binary thresholding applied to the H, S and B channels of the HSV and RGB color models. Finally, the tool segments all the positive areas and quantifies the brown density areas. Results A dataset of 879 TMA images were used to evaluate the methods. TMAs were prepared with an automatic tissue arrayer (Arraymold tool) composed of 50 holes/TMA with a cylinder diameter of 2mm. Slides were stained using different IHC stains, that is, CD1A, CD4, CD8, CD21, CD57, CD68, CD83, CD123, CK19, OXP3, LAMp3 and S100. The acquisition of the digital TMA images was done with Aperio ScanScope XT scanner at 40x (0.25 µm/ pixel). Afterwards, each cylinder image was individually extracted [3]. The use of color standardization makes the segmentation robust and free of parameter setting. Furthermore, the standardization process is able to reduce noise and facilitate the density quantification. The results were compared to manual density quantification by expert pathologists. The tests carried out provided up to 98% agreement when color standardization was applied against 90% without color standardization. The biggest error comes from FOXP3 samples.