scholarly journals Farbbasiertes Image Retrieval

2019 ◽  
Author(s):  
Thomas Kluth
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Color Spaces ◽  
Different Color

This (German) bachelor thesis discusses to what extent the feature color helps for content-based image retrieval. It analyses the use of different color spaces as well as different spatial segmentations of the image. It seems that color alone is not a very helpful feature for content-based image retrieval.

scholarly journals An Efficient Two-layer based Technique for Content-based Image Retrieval

2021 ◽  
Vol 5 (1) ◽  
pp. 28
Author(s):  
Fawzi Abdul Azeez Salih ◽  
Alan Anwer Abdulla
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Color Spaces ◽  
Query Image ◽  
Current State ◽  
Similar Images ◽  
Different Color

The rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain most similar images to the query image. In other words, first step aims to eliminate the most dissimilar images to the query image to reduce the range of search in the dataset of images. In the second layer, the query image is compared to the images obtained in the first layer based on extracting the (texture and color)-based features. The Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) were used as texture features. However, for the color features, three different color spaces were used, namely RGB, HSV, and YCbCr. The color spaces are utilized by calculating the mean and entropy for each channel separately. Corel-1K was used for evaluating the proposed approach. The experimental results prove the superior performance of the proposed concept of two-layer over the current state-of-the-art techniques in terms of precision rate in which achieved 82.15% and 77.27% for the top-10 and top-20, respectively.

scholarly journals A Study of Distance Metrics in Histogram Based Image Retrieval

2013 ◽  
Vol 4 (3) ◽  
pp. 821-830 ◽  
Author(s):  
Abhijeet Kumar Sinha ◽  
K.K. Shukla
Keyword(s):  
Image Retrieval ◽  
Digital Data ◽  
Content Based Image Retrieval ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
Color Spaces ◽  
Query Image ◽  
Histogram Intersection ◽  
Exhaustive Study ◽  
Different Color

There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image Retrieval (CBIR) is certainly the only solution to this problem. Difference between colors of two images can be an important metric to measure their similarity or dissimilarity. Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric.In this study, COREL Database is used for an exhaustive study of various distance metrics on different color spaces. Euclidean distance, Manhattan distance, Histogram Intersection and Vector Cosine Angle distances are used to compare histograms in both RGB and HSV color spaces. So, a total of 8 distance metrics for comparison of images for the sake of CBIR are discussed in this work.

scholarly journals Content Based Image Retrieval using Multi-level 3D Color Texture and Low Level Color Features with Neural Network Based Classification System

2021 ◽  
Vol 15 ◽  
pp. 265-270
Author(s):  
Priyesh Tiwari ◽  
Shivendra Nath Sharan ◽  
Kulwant Singh ◽  
Suraj Kamya
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Model ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Multi Level ◽  
Different Color ◽  
Color Texture

Content based image retrieval (CBIR), is an application of real-world computer vision domain where from a query image, similar images are searched from the database. The research presented in this paper aims to find out best features and classification model for optimum results for CBIR system.Five different set of feature combinations in two different color domains (i.e., RGB & HSV) are compared and evaluated using Neural Network Classifier, where best results obtained are 88.2% in terms of classifier accuracy. Color moments feature used comprises of: Mean, Standard Deviation,Kurtosis and Skewness. Histogram features is calculated via 10 probability bins. Wang-1k dataset is used to evaluate the CBIR system performance for image retrieval.Research concludes that integrated multi-level 3D color-texture feature yields most accurate results and also performs better in comparison to individually computed color and texture features.

COMPARATIVE ASSESSMENT OF CONTENT-BASED FACE IMAGE RETRIEVAL IN DIFFERENT COLOR SPACES

2005 ◽  
Vol 19 (07) ◽  
pp. 873-893 ◽  
Author(s):  
PEICHUNG SHIH ◽  
CHENGJUN LIU
Keyword(s):  
Face Recognition ◽  
Image Retrieval ◽  
Color Space ◽  
Principal Component ◽  
Face Image ◽  
Color Images ◽  
Grand Challenge ◽  
Color Spaces ◽  
The Face ◽  
Different Color

Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I1I2I3, HSI, and rgb) by evaluating seven color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB and RGB. Experimental results using 600 FERET color images corresponding to 200 subjects and 456 FRGC (Face Recognition Grand Challenge) color images of 152 subjects show that some color configurations, such as YV in the YUV color space and YI in the YIQ color space, help improve face retrieval performance.

scholarly journals Framework for Color and Texture Feature Fusion in Content Based Image Retrieval using Block Truncation Coding with Color Spaces

2020 ◽  
Vol 9 (3) ◽  
pp. 769-774
Keyword(s):  
Image Retrieval ◽  
Feature Fusion ◽  
Texture Feature ◽  
Local Binary Patterns ◽  
Input Image ◽  
Content Based Image Retrieval ◽  
Color Spaces ◽  
Query Image ◽  
Block Truncation Coding ◽  
Rate Of Increase

With tremendous growth in social media and digital technologies, generation, storing and transfer of huge amount of information over the internet is on the rise. Images or visual mode of communication have been prevailing and widely accepted as a mode of communication since ages. And with the growth of internet, the rate at which images are generated is growing exponentially. But the methods used to retrieve images are still very slow and inefficient, compared to the rate of increase in image databases. To cope up with this explosive increase in images, this information age has seen huge research advancement in Content Based Image Retrieval (CBIR). CBIR systems provide a way of utilizing the 3 major ways in which content is portrayed in images, those are shape, texture and color. In CBIR system, features are extracted from query image and similarity is found with features stored in database for retrieval. This provides an objective way of image retrieval, which is more efficient compared to subjective human annotation. Application specific CBIR systems have been developed and perform really well, but Generic CBIR systems are still under developed. Block Truncation Coding (BTC) has been chosen as a feature extractor. BTC applied directly on input image provides color content-based features of image and BTC applied after applying LBP on the image provide texture content-based features of image. Previous work consists of either color, shape or texture, but usage of more than one descriptor is still in research and might give better performance. The paper presents framework for color and texture feature fusion in content-based image retrieval using block truncation coding with color spaces. Experimentation is carried out on Wang Dataset of 1000 images consisting of 10 classes. Each class has 100 images in it. Obtained results have shown performance improvement using fusion of BTC extracted color features and texture features extracted with BTC applied on Local Binary Patterns (LBP). Conversion of color space from RGB to LUV is done using Kekre's LUV.

Sign in / Sign up

Export Citation Format

Share Document