Which Color Space Should Be Chosen for Robust Color Image Retrieval Based on Mixture Modeling

2014 ◽  
pp. 55-64 ◽  
Author(s):  
Maria Łuszczkiewicz-Piątek
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Mixture Modeling

ROBUST IMAGE RETRIEVAL APPROACH BASED ON CLUSTERING ANALYSIS OF RE-QUANTIZED COLOR IMAGE

2007 ◽  
Vol 05 (01) ◽  
pp. 213-225 ◽  
Author(s):  
YA-LI JI ◽  
XIAO-PING CHENG ◽  
NAI-QIN FENG
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Feature Representation ◽  
Hsv Color Space ◽  
Invariant Moments ◽  
Robust Image ◽  
Color Clustering ◽  
Object Features ◽  
Large Image Databases

In this paper, we propose a robust approach about color image retrieval. It can realize fast matching in CBIR (Content-Based Image Retrieval) when we search in large image databases. Indexes root in object features of Z image which is the result of re-quantization in HSV color space, matching with a non-geometrical distance is based on objects, so time consumption pixel by pixel can be avoided. Because Z image is made up of many color clustering regions and invariant moments are used for feature representation, our approach is robust to translation, scale and rotation.

Content-Based Image Retrieval Using Color Volume Histograms

2019 ◽  
Vol 33 (11) ◽  
pp. 1940010 ◽  
Author(s):  
Ji-Zhao Hua ◽  
Guang-Hai Liu ◽  
Shu-Xiang Song
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Content Based Image Retrieval ◽  
Feature Descriptor ◽  
Human Visual Perception ◽  
Hsv Color Space ◽  
Spatial Features ◽  
Close Relationship ◽  
New Feature

Human visual perception has a close relationship with the HSV color space, which can be represented as a cylinder. The question of how visual features are extracted using such an attribute is important. In this paper, a new feature descriptor; namely, a color volume histogram, is proposed for image representation and content-based image retrieval. It converts a color image from RGB color space to HSV color space and then uniformly quantizes it into 72 bins of color cues and 32 bins of edge cues. Finally, color volumes are used to represent the image content. The proposed algorithm is extensively tested on two Corel datasets containing 15[Formula: see text]000 natural images. These image retrieval experiments show that the color volume histogram has the power to describe color, texture, shape and spatial features and performs significantly better than the local binary pattern histogram and multi-texton histogram approaches.

scholarly journals Image Retrieval Based on a Multi-Integration Features Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Chu ◽  
Guang-Hai Liu
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Integration Theory ◽  
Feature Descriptor ◽  
Hsv Color Space ◽  
Color Differences ◽  
Feature Integration Theory ◽  
Perception Theory ◽  
Edge Features

Feature integration theory can be regarded as a perception theory, but the extraction of visual features using such a theory within the CBIR framework is a challenging problem. To address this problem, we extract the color and edge features based on a multi-integration features model and use these for image retrieval. A novel and highly simple but efficient visual feature descriptor, namely, a multi-integration features histogram, is proposed for image representation and content-based image retrieval. First, a color image is converted from the RGB to the HSV color space, and the color features and color differences are extracted. Then, the color differences are calculated to extract the edge features using a set of simple integration processes. Finally, combining the color, edge, and spatial layout features allows representing the image content. Experiments show that our method produces results comparable to existing and well-known methods on three datasets that contain 25,000 natural images. The performances are significantly better than that of the BOW histogram, local binary pattern histogram, histogram of oriented gradient, and multi-texton histogram, with performances similar to the color volume histogram.

Image Retrieval Technology Based on Color Similarity Matrix

2014 ◽  
Vol 912-914 ◽  
pp. 1272-1275
Author(s):  
Shui Li Zhang ◽  
Jun Tang Dong ◽  
Zhi Yong Feng
Keyword(s):  
Image Retrieval ◽  
Euclidean Distance ◽  
Color Image ◽  
Color Space ◽  
Experimental Result ◽  
Similarity Matrix ◽  
Similarity Matching ◽  
Hsv Color Space ◽  
Quantization Effect ◽  
Color Similarity

Somecolor quantization problems concerning quantization of a color image into a fewnumbers of colors are discussed. In the HSV color space that is the best visualidentity of people, H is uniform quantified, S and V is respectivelynon-uniform quantified.Aimed at the limitation of Euclidean distance failing to consider thesimilarity between colors in the process of retrieval, a color similaritymatrix is introduced. Experimental result shows this algorithm is of certainpracticability and the quantization effect is also comparatively ideal whenmaking similarity matching

Pencarian File Gambar Berdasarkan Dominasi Warna

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Vina Chovan Epifania ◽  
Eko Sediyono
Keyword(s):  
Digital Image ◽  
Retrieval System ◽  
Color Image ◽  
Color Space ◽  
Image Data ◽  
Image Search ◽  
Test Results ◽  
Application Form ◽  
Space Model ◽  
Image File

Abstract. Image File Searching Based on Color Domination. One characteristic of an image that can be used in image searching process is the composition of the colors. Color is a trait that is easily seen by man in the picture. The use of color as a searching parameter can provide a solution in an easier searching for images stored in computer memory. Color images have RGB values that can be computed and converted into HSL color space model. Use of HSL images model is very easy because it can be calculated using a percent, so that in each pixel of the image can be grouped and named, this can give a dominant values of the colors contained in one image. By obtaining these values, the image search can be done quickly just by using these values to a retrieval system image file. This article discusses the use of the HSL color space model to facilitate the searching for a digital image in the digital image data warehouse. From the test results of the application form, a searching is faster by using the colors specified by the user. Obstacles encountered were still searching with a choice of 15 basic colors available, with a limit of 33% dominance of the color image search was not found. This is due to the dominant color in each image has the most dominant value below 33%.   Keywords: RGB, HSL, image searching Abstrak. Salah satu ciri gambar yang dapat dipergunakan dalam proses pencarian gambar adalah komposisi warna. Warna adalah ciri yang mudah dilihat oleh manusia dalam citra gambar. Penggunaan warna sebagai parameter pencarian dapat memberikan solusi dalam memudahkan pencarian gambar yang tersimpan dalam memori komputer. Warna gambar memiliki nilai RGB yang dapat dihitung dan dikonversi ke dalam model HSL color space. Penggunaan model gambar HSL sangat mudah karena dapat dihitung dengan menggunakan persen, sehingga dalam setiap piksel gambar dapat dikelompokan dan diberi nama, hal ini dapat memberikan suatu nilai dominan dari warna yang terdapat dalam satu gambar. Dengan diperolehnya nilai tersebut, pencarian gambar dapat dilakukan dengan cepat hanya dengan menggunakan nilai tersebut pada sistem pencarian file gambar. Artikel ini membahas tentang penggunaan model HSL color space untuk mempermudah pencarian suatu gambar digital didalam gudang data gambar digital. Dari hasil uji aplikasi yang sudah dibuat, diperoleh pencarian yang lebih cepat dengan menggunakan pilihan warna yang ditentukan sendiri oleh pengguna. Kendala yang masih dijumpai adalah pencarian dengan pilihan 15 warna dasar yang tersedia, dengan batas dominasi warna 33% tidak ditemukan gambar yang dicari. Hal ini disebabkan warna dominan disetiap gambar kebanyakan memiliki nilai dominan di bawah 33%. Kata Kunci: RGB, HSL, pencarian gambar

EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER

2000 ◽  
Vol 14 (07) ◽  
pp. 963-978 ◽  
Author(s):  
HUA YANG ◽  
MASAAKI KASHIMURA ◽  
NORIKADU ONDA ◽  
SHINJI OZAWA
Keyword(s):  
Color Image ◽  
Color Space ◽  
Input Image ◽  
Color Image Processing ◽  
Front Cover ◽  
Stroke Width ◽  
Other Information ◽  
Region Extraction ◽  
Hsi Color Space ◽  
Rgb Color Space

This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The system consists of three major components: preprocessing, color space segmentation and text region extraction and classification. Preprocessing extracts the edge lines of the book and geometrically corrects and segments the input image, into the parts of front cover, spine and back cover. The same as all color image processing researches, the segmentation of color space is an essential and important step here. Instead of RGB color space, HSI color space is used in this system. The color space is segmented into achromatic and chromatic regions first; and both the achromatic and chromatic regions are segmented further to complete the color space segmentation. Then text region extraction and classification follow. After detecting fundamental features (stroke width and local label width) text regions are determined. By comparing the text regions on front cover with those on spine, all extracted text regions are classified into suitable bibliography categories: author, title, publisher and other information, without applying OCR.

A Cascade Color Image Retrieval Framework

2020 ◽  
pp. 23-36
Author(s):  
K. S. Gautam ◽  
Latha Parameswaran ◽  
Senthil Kumar Thangavel
Keyword(s):  
Image Retrieval ◽  
Color Image
Sign in / Sign up

Export Citation Format

Share Document