Pattern graph-based image retrieval system combining semantic and visual features

2017 ◽  
Vol 76 (19) ◽  
pp. 20287-20316
Author(s):  
Olfa Allani ◽  
Hajer Baazaoui Zghal ◽  
Nedra Mellouli ◽  
Herman Akdag
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Visual Features ◽  
Image Retrieval System ◽  
Pattern Graph

scholarly journals Color Based Image Retrieval by Combining Various Features

2019 ◽  
Vol 9 (2) ◽  
pp. 454-460
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Visual Features ◽  
Semantic Features ◽  
Retrieval Rate ◽  
Image Retrieval System ◽  
Mathematical Formulas ◽  
Image Set ◽  
Different Color ◽  
Image Collection

Content based image retrieval system retrieve the images according to the strong feature related to desire as color, texture and shape of an image. Although visual features cannot be completely determined by semantic features, but still semantic features can be integrate easily into mathematical formulas. This paper is focused on retrieval of images within a large image collection, based on color projection by applying segmentation and quantification on different color models and compared for good result. This method is applied on different categories of image set and evaluated its retrieval rate in different models

Multimedia Image Retrieval System by Combining CNN With Handcraft Features in Three Different Similarity Measures

2020 ◽  
Vol 10 (1) ◽  
pp. 1-23
Author(s):  
Maher Alrahhal ◽  
Supreethi K.P.
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Retrieval System ◽  
Similarity Measures ◽  
Visual Features ◽  
Classification Problems ◽  
Bag Of Features ◽  
Image Retrieval System ◽  
Multimedia Image ◽  
Medical Dataset

The authors propose WNAHVF, a combined weighted and normalized AlexNet with handcrafted visual features for extracting features from images and using those vectors for image retrieval and classification. The authors test the WNAHVF method on two general datasets, Corel-1k and Corel-10k, and one medical dataset. The outcomes demonstrate combining Bag of Features and Local Neighbor patterns with AlexNet enhances the accuracy and gives better results in general and medical image datasets in retrieval and classification problems. This algorithm gives results that are superior to existing strategies.

scholarly journals A Knowledge-based Image Retrieval System Integrating Semantic and Visual Features

2016 ◽  
Vol 96 ◽  
pp. 1428-1436 ◽  
Author(s):  
Olfa Allani ◽  
Hajer Baazaoui Zghal ◽  
Nedra Mellouli ◽  
Herman Akdag
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Visual Features ◽  
Knowledge Based ◽  
Image Retrieval System

scholarly journals CONTENT BASED IMAGE RETRIEVAL SYSTEM BY FUSION OF COLOR, TEXTURE AND EDGE FEATURES WITH SVM CLASSIFIER AND RELEVANCE FEEDBACK

2018 ◽  
Vol 6 (9) ◽  
pp. 259-273
Author(s):  
Priyanka Saxena ◽  
Shefali
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Image Retrieval System ◽  
Combined Features ◽  
Edge Features ◽  
Color Texture

Content Based Image Retrieval system automatically retrieves the most relevant images to the query image by extracting the visual features instead of keywords from images. Over the years, several researches have been conducted in this field but the system still faces the challenge of semantic gap and subjectivity of human perception. This paper proposes the extraction of low-level visual features by employing color moment, Local Binary Pattern and Canny Edge Detection techniques for extracting color, texture and edge features respectively. The combination of these features is used in conjunction with Support Vector Machine to reduce the retrieval time and improve the overall precision. Also, the challenge of semantic gap between low and high level features is addressed by incorporating Relevance Feedback. Average precision value of 0.782 was obtained by combining the color, texture and edge features, 0.896 was obtained by using combined features with SVM, 0.882 was obtained by using combined features with Relevance Feedback to overcome the challenge of semantic gap. Experimental results exhibit improved performance than other state of the art techniques.

scholarly journals Unsupervised Content Based Image Retrieval by Combining Visual Features of an Image With A Threshold

2012 ◽  
pp. 149-154
Author(s):  
S. M. Zakariya ◽  
Rashid Ali ◽  
Nesar Ahmad
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Database ◽  
The Other ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Query Image ◽  
System A ◽  
Image Retrieval System

Content-based image retrieval (CBIR) uses the visual features of an image such as color, shape and texture to represent and index the image. In a typical content based image retrieval system, a set of images that exhibit visual features similar to that of the query image are returned in response to a query. CLUE (CLUster based image rEtrieval) is a popular CBIR technique that retrieves images by clustering. In this paper, we propose a CBIR system that also retrieves images by clustering just like CLUE. But, the proposed system combines all the features (shape, color, and texture) with a threshold for the purpose. The combination of all the features provides a robust feature set for image retrieval. We evaluated the performance of the proposed system using images of varying size and resolution from image database and compared its performance with that of the other two existing CBIR systems namely UFM and CLUE. We have used four different resolutions of image. Experimentally, we find that the proposed system outperforms the other two existing systems in ecery resolution of image.

Research on Constructing a Multi-Label Image Annotation and Retrieval System

2010 ◽  
Vol 20-23 ◽  
pp. 559-564
Author(s):  
Yu Long Tian ◽  
Ran Li ◽  
Jian Jiang Lu ◽  
Ya Fei Zhang ◽  
Zi Ning Lu
Keyword(s):  
Genetic Algorithm ◽  
Image Retrieval ◽  
High Precision ◽  
Retrieval System ◽  
Image Annotation ◽  
Visual Features ◽  
Semantic Level ◽  
Optimal Weights ◽  
Image Retrieval System ◽  
Optimal Feature

In this paper, we design and construct a multi-label image annotation and retrieval system. Various MPEG-7 low level visual features are employed for representing images. For image annotation, a bi-coded genetic algorithm is employed to select optimal feature subsets and corresponding optimal weights for every one vs. one SVM classifiers. After an unlabelled image is segmented into several regions, pre-trained SVMs are used to annotate each region, final label is obtained by merging all the region labels. Based on multi-label of image, image retrieval system provides keyword-based image retrieval service. Multi-labels can provide abundant descriptions for image content in semantic level, high precision annotation algorithm further improve annotation performance.

A Content-Based Image Retrieval System with Image Semantic

2010 ◽  
Vol 159 ◽  
pp. 638-643
Author(s):  
Ying Ma ◽  
Lao Mo Zhang ◽  
Jin Xing Ma
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Fingerprint Recognition ◽  
Semantic Gap ◽  
Visual Features ◽  
Multimedia Technology ◽  
Retrieval Systems ◽  
Image Retrieval System ◽  
Object Ontology

With the development of information technology and multimedia technology, more and more images appear and have become a part of our daily life. Efficient image searching, storing, retrieval and browsing tools are in high need in various domains, including face and fingerprint recognition, publishing, medicine, architecture, remote sensing, fashion etc. Thus, many image retrieval systems have been developed to meet the need. The aim of content-based retrieval systems is to provide maximum support in bridging the semantic gap between the simplicity of available visual features and the richness of the user semantics. In this paper, we discuss the main technologies for reducing the semantic gap, namely, object-ontology, machine learning, relevance feedback.

Sign in / Sign up

Export Citation Format

Share Document