Dominant Color Features Research Articles

Implementation of Adaptive Real-Time Camouflage System in Visible-Light Band

This paper presents an adaptive camouflage system in visible band, featuring a dominant color feature-matching algorithm and pulse width modulation (PWM)-based display driving circuit. The control system consists of three parts, namely, the background sensing part, the central processing part, and the physical driving waveform generation part. Images of the local environment are sampled by the background sensing part, and then the dominant color feature matching algorithm is conducted to select a proper camouflage image that matches the local environment. Consequently, the cholesteric liquid crystals (CLCs) display using amplitude adjustable AC voltage, which is modulated by the physical driving waveform generation unit. The experimental results show that the matching degree of the proposed algorithm was 2.47 times that of the conventional hue (H), saturation (S), and value (V) histogram camouflage evaluation method, while the output peak wavelength of the reflective band can be adjusted from 604 to 544 nm according to the ambient color profile.

Automatic Extraction of Color Features from Landscape Images Based on Image Processing

The dominant color features determine the presentation effect and visual experience of landscapes. The existing studies rarely quantify the application effect of landscape colors through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space. To solve the problem, this paper proposes an automatic extraction method for color features from landscape images based on image processing. Firstly, a landscape lighting model was constructed based on color constancy theories, and the quality of landscape images was improved with color constant image enhancement technology. In this way, the low-level color features were extracted from the landscape image library. Next, support vector machine (SVM) and fuzzy c-means (FCM) were innovatively integrated to extract high-level color features from landscape images. The proposed method was proved effective through experiments.

An Improved Seed Point Selection-Based Unsupervised Color Clustering for Content-Based Image Retrieval Application

Abstract The images involved in the content-based image retrieval (CBIR) applications are collectively represented by features such as color, texture and shape. The precision of the CBIR application relies on the key features used in image representation and its similarity measure. In CBIR, dominant color feature extraction is affected by the predefined intervals used in color quantization. The proposed work mainly concentrates on extracting the dominant color information of the image using the clustering process. The clustering process is initiated by the proposed seed point’s selection approach. This approach derives the number of seed points using the first order statistical measure and maximum range of the distributed pixel values. Moreover, this work gives equal priority to dominant color and its occurrence information in calculating the similarity between query and database images. Finally, the standard databases such as SIMPLIcity, Corel-10k, OT-scene, Oxford flower and GHIM are taken to investigate the performance of the proposed dominant color based image retrieval application.

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.

Vehicle Ego-Localization Based on Streetscape Image Database Under Blind Area of Global Positioning System

Vehicle positioning is critical for inter-vehicle communication, navigation, vehicle monitoring and tracking. They are regarded as the core technology ensuring safety in everyday-driving. This paper proposes an enhanced vehicle ego-localization method based on streetscape image database. It is most useful in the global positioning system (GPS) blind area. Firstly, a database is built by collecting streetscape images, extracting dominant color feature and detecting speeded up robust feature (SURF) points. Secondly, an image that the vehicle shoots at one point is analyzed to find a matching image in the database by dynamic programming (DP) matching. According to the image similarity, several images with higher probabilities are selected to realize coarse positioning. Finally, different weights are set to the coordinates of the shooting location with the maximum similarity and its 8 neighborhoods according to the number of matching points, and then interpolating calculation is applied to complete accurate positioning. Experimental results show that the accuracy of this study is less than 1.5m and its running time is about 3.6 s. These are basically in line with the practical need. The described system has an advantage of low cost, high reliability and strong resistance to signal interference, so it has a better practical value as compared with visual odometry (VO) and radio frequency identification (RFID) based approach for vehicle positioning in the case of GPS not working.

From Signal to Image: Enabling Fine-Grained Gesture Recognition with Commercial Wi-Fi Devices.

Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in wireless sensing hold promise for a ubiquitous, non-invasive and low-cost system with existing Wi-Fi infrastructures. In this paper, we propose DeepNum, which enables fine-grained finger gesture recognition with only a pair of commercial Wi-Fi devices. The key insight of DeepNum is to incorporate the quintessence of deep learning-based image processing so as to better depict the influence induced by subtle finger movements. In particular, we make multiple efforts to transfer sensitive Channel State Information (CSI) into depth radio images, including antenna selection, gesture segmentation and image construction, followed by noisy image purification using high-dimensional relations. To fulfill the restrictive size requirements of deep learning model, we propose a novel region-selection method to constrain the image size and select qualified regions with dominant color and texture features. Finally, a 7-layer Convolutional Neural Network (CNN) and SoftMax function are adopted to achieve automatic feature extraction and accurate gesture classification. Experimental results demonstrate the excellent performance of DeepNum, which recognizes 10 finger gestures with overall accuracy of 98% in three typical indoor scenarios.

Automatic texture exemplar extraction based on global and local textureness measures

Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques emphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, obtaining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant postprocessing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly crop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured patches from non-textured patches, in conjunction with SVM prediction. To identify real texture exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure (using binary Gabor pattern (BGP)) and dominant color features (using color histograms) between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures. We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures manually selected by an artist and a statistical study demonstrate its effectiveness.

Dominant color and texture feature extraction for banknote discrimination

Banknote discrimination with image recognition technology is significant in many applications. The traditional methods based on image recognition only recognize the banknote denomination without discriminating the counterfeit banknote. To solve this problem, we propose a systematical banknote discrimination approach with the dominant color and texture features. After capturing the visible and infrared images of the test banknote, we first implement the tilt correction based on the principal component analysis (PCA) algorithm. Second, we extract the dominant color feature of the visible banknote image to recognize the denomination. Third, we propose an adaptively weighted local binary pattern with “delta” tolerance algorithm to extract the texture features of the infrared banknote image. At last, we discriminate the genuine or counterfeit banknote by comparing the texture features between the test banknote and the benchmark banknote. The proposed approach is tested using 14,000 banknotes of six different denominations from Chinese yuan (CNY). The experimental results show 100% accuracy for denomination recognition and 99.92% accuracy for counterfeit banknote discrimination.

On Branded Handbag Recognition

Manufacturing branded handbags is a big business in the fashion world. Shoppers’ feedback showing photos of their purchased handbags in social networks or blogs is important for branding purposes. In this paper, we deal with handbag recognition. It is a challenging problem due to the inter-class style similarity and the intra-class color variation. We focus on developing discriminative representations of handbag style and color. For handbag style representation, two supervised mid-level patch selection procedures are proposed to select discriminative patches, regarding individual classes and pairwise classes. We also propose a low-level complementary feature, extracted from texture-enhanced mid-level patches, to capture the fine details of the mid-level patches. For handbag color representation, we propose to extract dominant color features to handle the illumination changes. The performance of our proposed method is evaluated on a newly built branded handbag dataset. The results show that our method performs favorably in recognizing handbags, with around $10\%$ improvement in accuracy when compared with the existing fine-grained or generic object recognition methods.

Fast Image Retrieval Prototype using Color Descriptor

In recent years, image retrieval prototypes become important and increased noticeably. Color feature is one of the most significant features to represent image. In this paper, we use a Dominant Color (DC) feature to represent images where each image represented by 8-DCs as maximum. Based on DCs values, image database is indexed using 3-D RGB partitioning color space. This is to reduce searching process where once a query image is given to the prototype; it will not search the whole database. Proposed technique will identify the partition and search the image within this partition only. According to the proposed method, extensive experiments were conducted on Corel databases. As a result, the retrieval time is reduced significantly without degradation to precision of retrieval.

Exploring the color feature power for psoriasis risk stratification and classification: A data mining paradigm

A large percentage of dermatologist׳s decision in psoriasis disease assessment is based on color. The current computer-aided diagnosis systems for psoriasis risk stratification and classification lack the vigor of color paradigm. The paper presents an automated psoriasis computer-aided diagnosis (pCAD) system for classification of psoriasis skin images into psoriatic lesion and healthy skin, which solves the two major challenges: (i) fulfills the color feature requirements and (ii) selects the powerful dominant color features while retaining high classification accuracy.Fourteen color spaces are discovered for psoriasis disease analysis leading to 86 color features. The pCAD system is implemented in a support vector-based machine learning framework where the offline image data set is used for computing machine learning offline color machine learning parameters. These are then used for transformation of the online color features to predict the class labels for healthy vs. diseased cases. The above paradigm uses principal component analysis for color feature selection of dominant features, keeping the original color feature unaltered. Using the cross-validation protocol, the above machine learning protocol is compared against the standalone grayscale features with 60 features and against the combined grayscale and color feature set of 146.Using a fixed data size of 540 images with equal number of healthy and diseased, 10 fold cross-validation protocol, and SVM of polynomial kernel of type two, pCAD system shows an accuracy of 99.94% with sensitivity and specificity of 99.93% and 99.96%. Using a varying data size protocol, the mean classification accuracies for color, grayscale, and combined scenarios are: 92.85%, 93.83% and 93.99%, respectively. The reliability of the system in these three scenarios are: 94.42%, 97.39% and 96.00%, respectively. We conclude that pCAD system using color space alone is compatible to grayscale space or combined color and grayscale spaces. We validated our pCAD system against facial color databases and the results are consistent in accuracy and reliability.

Color Extraction and Research of Image Retrieval Based on Wavelet Territory

This paper firstly studies the image color features based on wavelet territory. We introduce a color features’ extract method based on HSI low-frequency subband color features after partition. Firstly, according to the image attention from human eyes, we split the image into sub-blocks. Then extract HSI low-frequency subband color features of each sub-block after wavelet transform, and we can obtain the image color features by weighting. Comparing with traditional histogram method, the experiment results show that the proposed algorithm based on weighted dominant color feature has better retrieval precision.

An Image Retrieval Algorithm Based on HVS Weighted Color Features

This paper introduces a HVS Weighted color features’ extract method. Firstly, we split the image into sub-blocks and draw the color feature consists of dominant colors in each sub-block. Then weighting the gained color features by making use of Human Visual System. So we can obtain the weighted dominant color feature. Comparing with traditional histogram method and split blocks dominant color method, the experiment results show that the proposed algorithm based on weighted dominant color feature has better retrieval precision.

A novel framework for CBCD using integrated color and acoustic features

Most studies in content-based video copy detection (CBCD) concentrate on visual signatures, while only very few efforts are made to exploit audio features. The audio data, if present, is an essential source of a video; hence, the integration of visual-acoustic fingerprints significantly improves the copy detection performance. Based on this aspect, we propose a new framework, which jointly employs color-based visual features and audio fingerprints for detecting the duplicate videos. The proposed framework incorporates three stages: First, a novel visual fingerprint based on spatio-temporal dominant color features is generated; Second, mel-frequency cepstral coefficients are extracted and compactly represented as acoustic signatures; Third, the resultant multimodal signatures are jointly used for the CBCD task, by employing combination rule and weighting strategies. The results of experiments on TRECVID 2008 and 2009 datasets, demonstrate the improved efficiency of the proposed framework compared to the reference methods against a wide range of video transformations.

An improved SOM algorithm and its application to color feature extraction

Reducing the redundancy of dominant color features in an image and meanwhile preserving the diversity and quality of extracted colors is of importance in many applications such as image analysis and compression. This paper presents an improved self-organization map (SOM) algorithm namely MFD-SOM and its application to color feature extraction from images. Different from the winner-take-all competitive principle held by conventional SOM algorithms, MFD-SOM prevents, to a certain degree, features of non-principal components in the training data from being weakened or lost in the learning process, which is conductive to preserving the diversity of extracted features. Besides, MFD-SOM adopts a new way to update weight vectors of neurons, which helps to reduce the redundancy in features extracted from the principal components. In addition, we apply a linear neighborhood function in the proposed algorithm aiming to improve its performance on color feature extraction. Experimental results of feature extraction on artificial datasets and benchmark image datasets demonstrate the characteristics of the MFD-SOM algorithm.

Facial image medical analysis system using quantitative chromatic feature

In order to investigate whether the appearance of a human face can be utilized for diagnostic purposes, which have been practiced for thousands of years in Traditional Chinese Medicine (TCM), this paper aims to present a computerized facial image analysis system by using quantitative chromatic features for disease diagnosis applications. A face image acquisition device is dedicatedly designed to acquire image samples from volunteers who have three types of health conditions: normal health, icterohepatitis, and severe hepatitis. Then, after color calibration on the acquired images to remove noises caused by lighting fluctuations, quantitative dominant color features are extracted by fuzzy clustering method. In order to further improve the diagnosis accuracy, a feature selection procedure is involved to identify the most discriminative feature subset for the diagnostic classification. Lastly, based on these selected quantitative feature, each face image could be diagnosed into different health groups. Experiments are conducted based on a database which includes over 300 sample images, and the result shows that the overall diagnosis accuracy between healthy samples and other two diseases is higher than 88%. Hence the feasibility of disease diagnosis by inspecting the chromatic feature of human face could be verified.

An Effective Similarity Measurement Algorithm for Dominant Color Feature Matching in Image Retrieval

For the reason that dominant colors can characterize color information of image region and can represent the image using fewer dimensions, it is one of the widely used color features in image retrieval. We extract the dominant color feature in HSV color space, and combine it with color distribution information. In this paper, a new similarity measurement algorithm based on block distance is proposed for dominant color matching. Our proposed algorithm not only takes the distance between dominant colors into account, but also the difference of the percentage of dominant colors. The average precision of our algorithm improves about 5% and about 14% respectively compared with block distance and Euclidean distance. Although the average precision of our algorithm is almost equal to quadratic form distance, the computation cost of our algorithm is obviously less than it.

CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features

There is a great need of developing efficient content based image retrieval systems because of the availability of large image databases. A new image retrieval system CTDCIRS (colortexture and dominant color based image retri eval system) to retrieve the images using three features called dynamic dominant color (DDC), Motif cooccurrence m atrix (MCM) and difference between pixels of scan pattern (DBPSP) is proposed. Initially the image is divided into eight coarse partitions using the fast color quantization algorithm and the eight dominant colors are obtained from eight partitions. Next the texture of the image is represented by the MCM and DBPSP. MCM is derived using a motif transformed image. MCM is similar to color cooccur rence matrix (CCM). MCM is the conventional pattern co occurrence matrix that calculates the probability of the occurrence of same pixel color between each pixel and its adjacent ones in each image, and this probability is considered as the attribute of the image.MCM captures third order image statistics in the local neighborhood which describes the direction of textures but not the complexity of the textures. That is why the DBPSP is also considered as one of the texture features. The three features Dominant color, MCM and DBPSP are integrated to facilitate the image retrieval system. Experimental results show that the proposed image retrieval is more efficient in retrieving the user interested images.