scholarly journals A Study of the Effect of Color Quantization Schemes for Different Color Spaces on Content-based Image Retrieval

2014 ◽  
Vol 96 (12) ◽  
pp. 1-8
Author(s):  
Moheb R.Girgis ◽  
Mohammed S. Reda
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Color Spaces ◽  
Color Quantization ◽  
Different Color

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 Color Quantization and Angle Representation

2014 ◽  
Vol 13 (10) ◽  
pp. 5094-5104
Author(s):  
Ihab Zaqout
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Measurement Methods ◽  
Content Based Image Retrieval ◽  
Similarity Measurement ◽  
Color Quantization ◽  
Human Visual Perception ◽  
Hsv Color Space ◽  
Perception System ◽  
Similar Images

An efficient non-uniform color quantization and similarity measurement methods are proposed to enhance the content-based image retrieval (CBIR) applications. The HSV color space is selected because it is close to human visual perception system, and a non-uniform color method is proposed to quantize an image into 37 colors. The marker histogram (MH) vector of size 296 values is generated by segmenting the quantized image into 8 regions (multiplication of 45°) and count the occurrences of the quantized colors in their particular angles. To cope with rotated images, an incremental displacement to the MH is applied 7 times. To find similar images, we proposed a new similarity measurement and other 4 existing metrics. A uniform color quantization of related work is implemented too and compared to our quantization method. One-hundred test images are selected from the Corel-1000 images database. Our experimental results conclude high retrieving precision ratios compared to other techniques.

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.

scholarly journals CONTENT-BASED IMAGE RETRIEVAL UNTUK PENCARIAN PRODUK PONSEL

2019 ◽  
Vol 3 (1) ◽  
pp. 31
Author(s):  
Nickolas Cornelius Siantar ◽  
Jaqnson Hendryli ◽  
Dyah Erny Herwindiati
Keyword(s):  
Image Retrieval ◽  
Vector Quantization ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Content Based Image Retrieval ◽  
Color Quantization ◽  
Histogram Of Oriented Gradient ◽  
Feature Extractor ◽  
Online Stores

Phone or smartphone and online shop, there is something that cannot be separated with human. There are so many type of smartphones show up in the market that people are confused on which one to get on the online stores. Smartphones recognition is done by using the Histogram of Oriented Gradient to recognize shapes of phones, Color Quantization to recognize the color, and Local Binary Pattern to recognize texture of the phones. The output of the Feature Extractor is a feature vector which is used on the LVQ to process recognize through finding the smallest Euclidean Distance between the trained vectors. The result of this paper is an application that can recognize 16 phone types using the image with the accuracy of 9.6%. Pada saat ini, ponsel dan toko online merupakan sesuatu yang tidak dapat dipisahkan dari manusia. Begitu banyak jenis ponsel bermunculan setiap tahunnya sehingga menyebabkan manusia bingung dalam mengenali ponsel tersebut. Pada program pengenalan ponsel ini digunakan Histogram of Oriented Gradient untuk mengambil fitur berupa bentuk ponsel, Color Quantization untuk mengambil fitur warna, dan Local Binary Pattern untuk mengambil fitur tekstur ponsel. Hasil dari pengambilan fitur berupa fitur vektor yang digunakan pada Learning Vector Quantization untuk proses pengenalan dengan mencari nilai terkecil Euclidean Distance antara vektor fitur dengan vektor bobot terlatih. Hasil dari program pengenalan ini yaitu program dapat melakukan pengenalan terhadap 16 jenis ponsel dengan akurasi sebesar 9.6%.

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