Color Features and Color Spaces Applications to the Automatic Image Annotation

Biometrics ◽  
2017 ◽  
pp. 1061-1083
Author(s):  
Vafa Maihami ◽  
Farzin Yaghmaee
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Image Annotation ◽  
Color Histogram ◽  
Experimental Results ◽  
Automatic Image Annotation ◽  
Color Spaces ◽  
Color Features ◽  
Different Color ◽  
Color Moment

Nowadays images play a crucial role in different fields such as medicine, advertisement, education and entertainment. Describing images content and retrieving them are important fields in image processing. Automatic image annotation is a process which produces words from a digital image based on the content of this the image by using a computer. In this chapter, after an introduction to neighbor voting algorithm for image annotation, we discuss the applicability of color features and color spaces in automatic image annotation. We discuss the applicability of three color features (color histogram, color moment and color Autocorrelogram) and three color spaces (RGB, HSI and YCbCr) in image annotation. Experimental results, using Corel5k benchmark annotated images dataset, demonstrate that using different color spaces and color features helps to select the best color features and spaces in image annotation area.

Color Features and Color Spaces Applications to the Automatic Image Annotation

2016 ◽  
pp. 378-400 ◽  
Author(s):  
Vafa Maihami ◽  
Farzin Yaghmaee
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Image Annotation ◽  
Color Histogram ◽  
Experimental Results ◽  
Automatic Image Annotation ◽  
Color Spaces ◽  
Color Features ◽  
Different Color ◽  
Color Moment

Nowadays images play a crucial role in different fields such as medicine, advertisement, education and entertainment. Describing images content and retrieving them are important fields in image processing. Automatic image annotation is a process which produces words from a digital image based on the content of this the image by using a computer. In this chapter, after an introduction to neighbor voting algorithm for image annotation, we discuss the applicability of color features and color spaces in automatic image annotation. We discuss the applicability of three color features (color histogram, color moment and color Autocorrelogram) and three color spaces (RGB, HSI and YCbCr) in image annotation. Experimental results, using Corel5k benchmark annotated images dataset, demonstrate that using different color spaces and color features helps to select the best color features and spaces in image annotation area.

Air Quality Evaluation Based on Local Normalized Image Contrast

2014 ◽  
Vol 511-512 ◽  
pp. 413-416 ◽  
Author(s):  
Wen Ming Yang ◽  
Xiang Chen ◽  
Qing Min Liao
Keyword(s):  
Image Processing ◽  
Air Quality ◽  
Digital Image ◽  
Quality Evaluation ◽  
Image Contrast ◽  
Experimental Results ◽  
Image Blurring ◽  
Pm2.5 And Pm10

Air quality has been paid increasingly attention, and air quality evaluation via digital image technology can help people know the air quality conveniently. Visible pollutants can affect the degree of image blurring, so it is possible to evaluate the density of these pollutants using image processing. We adopt a local normalized image contrast and attempt to explore its relationship with the values of PM2.5 and PM10. Experimental results demonstrate the proposed measure is promising.

Study on Reproduction of Color Information of Digital Image Based on Different Color Gamut

2012 ◽  
Vol 430-432 ◽  
pp. 838-841
Author(s):  
Wen Ge Chen
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Color Space ◽  
Mapping Method ◽  
Color Information ◽  
Color Gamut ◽  
Gamut Mapping ◽  
Processing Software ◽  
Image Processing Software ◽  
Different Color

This paper is based on digital image color information reproduction error in a different color gamut,Through the different color gamut mapping method, image processing software Photoshop is used to make experiment and to obtain the corresponding image effect. Using digital presses to print out and use Spectrodensitometer measure the corresponding data.Using Excel software for data processing and analysis, digital image color information of loss situation is obtained in RGB and CMYK color space, It can provide certain basis for control of the color loss.

A Self-Environment Adaption Model for Cross-Media Reproduction of Digital Image

2012 ◽  
Vol 262 ◽  
pp. 134-137
Author(s):  
Xiang Yang Xu ◽  
Li Jie Wang ◽  
Qiao Chen
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Image Display ◽  
Experimental Results ◽  
Ambient Light ◽  
Cross Media ◽  
Color Conversion

A self- environment adaption model for cross-media reproduction of digital image is presented in this paper.This model is used to color conversion, makes image adaptive display according to ambient light changes. Experimental results show that this model can be used in all kinds of image processing systems for the displaying of image, particularly suitable for handheld image display apparatus.

The Algorithm to Measure Crack Width with Incircle

2013 ◽  
Vol 684 ◽  
pp. 481-485 ◽  
Author(s):  
Bao Zhen Ge ◽  
Qi Jun Luo ◽  
Bin Ma ◽  
Yong Jie Wei ◽  
Bo Chen ◽  
...  
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Crack Width ◽  
Experimental Results ◽  
Average Width ◽  
Center Of A Group ◽  
Major Defect ◽  
Skeleton Point

Crack is a major defect of buildings. Digital image methods are often used to detect cracks. But incorrect or un-unique results may be inverted with an inappropriate algorithm. An image processing way is presented to obtain the sole width value. Meanwhile, the crack with several branches can be measured. In the processing, the crack skeleton is first calculated. Then each of the points on the skeleton is served as a center of a group of circles, one by one. The radius of the circles is increased step by step. The iterations will not stop until any point in the circle goes out of the crack. Thus the last circle in the iteration is served as an incircle of the crack. The diameter of the incircle is a crack width in a given skeleton point. The maximal and average width of the crack will be calculated after all the incircles with all the skeleton point are traversed. The experimental results show the proposed method can extract the width of cracks in a complex context.

Local Statistic Enhancement of Digital Image Based on Genetic Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1836-1840 ◽  
Author(s):  
Yong Zhuo Wu ◽  
Zhen Tu ◽  
Lei Liu
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Experimental Results ◽  
Computer Application ◽  
Novel Method ◽  
New Research

Iamge repair using the digital image processing technology has become a new research point in computer application. A novel method of local statistic enhancement based on genetic algorithm is proposed in this paper for the image enhancement. The modified amplified function are used as the jugement criterion, and the optimal paremeters are searched by the genetic algorithm. Experimental results show that the quality of images is improved dramatically by using this method.

scholarly journals Comparative Assessment of the Image Divide and Link Algorithm in Different Color Spaces

2018 ◽  
Vol 72 (1) ◽  
pp. 31-41
Author(s):  
Carely Guada ◽  
Daniel Gómez ◽  
J. Tinguaro Rodríguez ◽  
Javier Montero
Keyword(s):  
Digital Image ◽  
Color Space ◽  
Comparative Assessment ◽  
Dissimilarity Measure ◽  
Color Spaces ◽  
Spanning Forest ◽  
Different Color ◽  
Edge Based

Abstract In this paper, a comparative assessment of the Image Divide and Link Algorithm (ID&L) in different color spaces is presented. This, in order to show the significance of choosing a specific color space when the algorithm computes the dissimilarity measure between adjacent pixels. Specifically, the algorithm procedure is based on treating a digital image as a graph, assigning a weight to each edge based on the dissimilarity measure between adjacent pixels. Then, the algorithm constructs a spanning forest through a Kruskal scheme to order the edges successively while partitions are obtained. This process is driven until all the pixels of the image are segmented, that is, there are as many regions as pixels. The results of the algorithm which have been compared with those generated using different color spaces are shown.

Color Based Oil Palm Crop Bunch Grading Using Probabilistic Neural Network

2020 ◽  
Vol 17 (9) ◽  
pp. 4141-4144
Author(s):  
S. Siddesha ◽  
S. K. Niranjan
Keyword(s):  
Neural Network ◽  
Oil Palm ◽  
Probabilistic Neural Network ◽  
Color Histogram ◽  
Feature Models ◽  
Color Features ◽  
Image Dataset ◽  
Color Correlogram ◽  
Rgb Images ◽  
Different Color

This work aims at grading the oil palm crop bunch in to three categories unripe, ripe and overripe. Different color feature models like color histogram, color moments, color correlogram and color coherence vector are used to extract the color features of the crop bunch. Oil palm crop bunches are classified into above mentioned grades using Probabilistic Neural Network. Experimentation is carried out using image dataset of 300 RGB images across three categories. An accuracy of 98.33% is achieved with 70% training, 10% validation and 20% testing for Color Coherence Vector features.

ARTISTIC MOSAIC SERIES GENERATION

2006 ◽  
Vol 06 (01) ◽  
pp. 139-154 ◽  
Author(s):  
XUELONG LI ◽  
YUAN YUAN ◽  
DACHENG TAO
Keyword(s):  
Image Processing ◽  
Effective Means ◽  
Mean Value ◽  
Experimental Results ◽  
Basic Algorithm ◽  
New Approach ◽  
Color Features ◽  
The Mean ◽  
The Impact ◽  
New Location

Human tastes in art motivate the need for effective means to build a visual mosaic picture which is made up of many small tiles. In some previous work researchers have tried to translate pictures' styles.1 In this paper, a new approach to image processing in arts is presented in the domain of generating a series of artistic mosaic pictures. An arbitrary image is first translated into a mosaic-based one by dividing it into a number of sub-blocks each with the mean value of the pixels in it. Each of these small fragments can be assigned a new location within this image so that a new mosaic picture is generated. By this mean, images with similar color features can be used to create a series of mosaic pictures. The mosaic pictures consist of the same elements as each other, but might be extremely different from the semantic contents. A basic algorithm is presented, followed by some further improvements. Some preliminary experimental results are then given to show the impact of the proposed special techniques.

scholarly journals Art Image Processing and Color Objective Evaluation Based on Multicolor Space Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Liang Jing ◽  
Shifeng Lv
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Objective Evaluation ◽  
Supplementary Information ◽  
Model Output ◽  
Color Spaces ◽  
Different Color

A convolutional neural network’s weight sharing feature can significantly reduce the cumbersome degree of the network structure and reduce the number of weights that need to be trained. The model can directly input the original image, without the process of feature extraction and data reconstruction in common classification algorithms. This kind of network structure has got a good performance in image processing and recognition. Based on the color objective evaluation method of the convolutional neural network, this paper proposes a convolutional neural network model based on multicolor space and builds a convolutional neural network based on VGGNet (Visual Geometry Group Net) in three different color spaces, namely, RGB (Red Green Blue), LAB (Luminosity a b), and HSV (Hue Saturation Value) color spaces. We carry out research on data input processing and model output selection and perform feature extraction and prediction of color images. After a model output selection judger, the prediction results of different color spaces are merged and the final prediction category is output. This article starts with the multidimensional correlation for visual art image processing and color objective evaluation. Considering the relationship between the evolution of artistic painting style and the color of artistic images, this article explores the characteristics of artistic image dimensions. In view of different factors, corresponding knowledge extraction strategies are designed to generate color label distribution, provide supplementary information of art history for input images, and train the model on a multitask learning framework. In this paper, experiments on multiple art painting data sets prove that this method is superior to single-color label classification methods.

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