Sternum Image Retrieval Based on High-level Semantic Information and Low-level Features

2008 ◽  
Author(s):  
Qin Chen ◽  
Xiaoying Tai
Keyword(s):  
Image Retrieval ◽  
Semantic Information ◽  
Low Level ◽  
High Level

scholarly journals Bag-of-Words Representation in Image Annotation: A Review

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Chih-Fong Tsai
Keyword(s):  
Image Retrieval ◽  
Image Annotation ◽  
Classification Problem ◽  
Feature Representation ◽  
Future Research ◽  
Bag Of Words ◽  
Low Level ◽  
Learning Techniques ◽  
Class Labels ◽  
High Level

Content-based image retrieval (CBIR) systems require users to query images by their low-level visual content; this not only makes it hard for users to formulate queries, but also can lead to unsatisfied retrieval results. To this end, image annotation was proposed. The aim of image annotation is to automatically assign keywords to images, so image retrieval users are able to query images by keywords. Image annotation can be regarded as the image classification problem: that images are represented by some low-level features and some supervised learning techniques are used to learn the mapping between low-level features and high-level concepts (i.e., class labels). One of the most widely used feature representation methods is bag-of-words (BoW). This paper reviews related works based on the issues of improving and/or applying BoW for image annotation. Moreover, many recent works (from 2006 to 2012) are compared in terms of the methodology of BoW feature generation and experimental design. In addition, several different issues in using BoW are discussed, and some important issues for future research are discussed.

scholarly journals Combining visual features and contextual information for image retrieval and annotation

2021 ◽  
Author(s):  
Rui Zhang
Keyword(s):  
Image Retrieval ◽  
Image Annotation ◽  
Bayesian Framework ◽  
Superior Performance ◽  
Visual Features ◽  
Feature Combination ◽  
Low Level ◽  
Combination Methods ◽  
High Level ◽  
Visual Domain

This thesis is primarily focused on the information combination at different levels of a statistical pattern classification framework for image annotation and retrieval. Based on the previous study within the fields of image annotation and retrieval, it has been well-recognized that the low-level visual features, such as color and texture, and high-level features, such as textual description and context, are distinct yet complementary in terms of their distributions and the corresponding discriminative powers of dealing with machine-based recognition and retrieval tasks. Therefore, effective feature combination for image annotation and retrieval has become a desirable and promising perspective from which the semantic gap can be further bridged. Motivated by this fact, the combination of the visual and context modalities and that of different features in the visual domain are tackled by developing two statistical patterns classification approaches considering that the features of the visual modality and those across different modalities exhibit different degrees of heterogeneities, and thus, should be treated differently. Regarding the cross-modality feature combination, a Bayesian framework is proposed to integrate visual content and context, which has been applied to various image annotation and retrieval frameworks. In terms of the combination of different low-level features in the visual domain, the problem is tackled with a novel method that combines texture and color features via a mixture model of their joint distribution. To evaluate the proposed frameworks, many different datasets are employed in the experiments, including the COREL database for image retrieval and the MSRC, LabelMe, PASCAL VOC2009, and an animal image database collected by ourselves for image annotation. Using various evaluation criteria, the first framework is shown to be more effective than the methods purely based on the low-level features or high-level context. As for the second, the experimental results demonstrate not only its superior performance to other feature combination methods but also its ability to discover visual clusters using texture and color simultaneously. Moreover, a demo search engine based on the Bayesian framework is implemented and available online.

Text-Based Image Retrieval

2018 ◽  
pp. 1-38
Keyword(s):  
Image Retrieval ◽  
Semantic Information ◽  
Retrieval System ◽  
Web Documents ◽  
Tag Ranking ◽  
High Level

The role of textual keywords for capturing the high-level semantics of an image in HTML document is studied. It is observed that the keywords present in HTML documents can be effectively used for describing the high-level semantics of the images appear in the same document. Techniques for processing HTML documents and Tag Ranking for Image Retrieval (TRIR) is explained for capturing semantic information about the images for retrieval applications. A retrieval system returns a large number of images for a query and hence it is difficult to display the most relevant images in top results. This chapter presents newly developed method for ranking the images in Web documents based on the properties of HTML TAGS in web documents for image retrieval from WWW.

The Imapct of Low-Level Features in Semantic-Based Image

2011 ◽  
pp. 23-45 ◽  
Author(s):  
Konstantinos Konstantinidis ◽  
Antonios Gasteratos ◽  
Ioannis Andreadis
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Experimental Results ◽  
Image Feature ◽  
Statistical Tool ◽  
Image Feature Extraction ◽  
Low Level ◽  
High Level

Image Retrieval (IR) is generally known as a collection of techniques for retrieving images on the basis of features, either low-level (Content-based IR) or high-level (Semantic-based IR). Since Semantic-based features rely on low-level ones, in this chapter the reader is initially familiarized with the most widely used low-level features. An efficient way to present these features is by means of a statistical tool capable of bearing concrete information, such as the histogram. For use in IR, the histograms extracted from the previously mentioned features need to be compared by means of a metric. The most popular methods and distances are, thus, apposed. Finally, a number of IR systems using histograms are presented in a thorough manner and their experimental results are discussed. The steps in order to develop a custom IR system, along with modern techniques in image feature extraction are also presented.

scholarly journals A content-based image retrieval system for hat database

2021 ◽  
Author(s):  
Danhua Li
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Gaussian Mixture ◽  
Middle Level ◽  
Content Based Image Retrieval ◽  
Shape Features ◽  
Fourier Descriptor ◽  
Low Level ◽  
Two Component ◽  
High Level

Content-based image retrieval (CBIR) is a technique for indexing and retrieving images based on the low-level features, middle-level features, and high-level features. Low-level feature is extracted from contents of the images such as color, texture and shape; middle-level feature is a region obtained as a result of image segmentation; high-level feature is semantic information about the meaning of image, its objects and their roles, and categories to which the image belongs. In this project, three low-level features texture-based retrieval, color-based retrieval and shape-based retrieval are implemented and compared on hat database. Texture features are obtained from parameters of a two-component Gaussian mixture model (GMM) in the wavelet domain. Color features are extracted from a two-component GMM on HLS color space. Shape features are extracted from the contour by using centroid-contour distance Fourier descriptor. A comprehensive experimental evaluation of the retrieval performance of different feature sets is performed. The experimental results indicate that the shape features based on the centroid-contour distance Fourier descriptor perform much better than the color and texture features for the hat database used in this project

Review of CBIR Related with Low Level and High Level Features

2016 ◽  
Vol 7 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Tamil Kodi ◽  
G. Rosline Nesa Kumari ◽  
S. Maruthu Perumal
Keyword(s):  
Image Retrieval ◽  
User Interaction ◽  
Standard Analysis ◽  
Query Image ◽  
Low Level ◽  
Comprehensive Comparison ◽  
High Level ◽  
High Level Feature ◽  
Good Manner

The method of retrieving pictures from the massive image info is termed as content based mostly image retrieval (CBIR). CBIR is that the standard analysis space of interest. CBIR paves the approach of user interaction with giant info by satisfying their queries within the sort of pictures. This paper discusses the recital of a CBIR system that is in and of itself repressed by the options adopted to symbolize the pictures within the record and conjointly study the approaches of a spread of ways that deals with the extraction of options supported low and high level options of images with the query image provided. The most contribution of this work could be a comprehensive comparison between the low level and high level feature approaches to CBIR.To retrieve the pictures in a good manner this paper provides associate platform for victimization the ways which can able to specialize in each low level and high level options and created clarification regarding high level options will retrieve images a lot of relevant to the query image provided.

A Multi-Label Image Annotation With Multi-Level Tagging System

2018 ◽  
pp. 28-47
Author(s):  
Kalaivani Anbarasan ◽  
Chitrakala S.
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Annotation ◽  
Image Features ◽  
Semantic Gap ◽  
Low Level ◽  
Image Retrieval System ◽  
Multi Level ◽  
High Level ◽  
Tagging System

The content based image retrieval system retrieves relevant images based on image features. The lack of performance in the content based image retrieval system is due to the semantic gap. Image annotation is a solution to bridge the semantic gap between low-level content features and high-level semantic concepts Image annotation is defined as tagging images with a single or multiple keywords based on low-level image features. The major issue in building an effective annotation framework is the integration of both low level visual features and high-level textual information into an annotation model. This chapter focus on new statistical-based image annotation model towards semantic based image retrieval system. A multi-label image annotation with multi-level tagging system is introduced to annotate image regions with class labels and extract color, location and topological tags of segmented image regions. The proposed method produced encouraging results and the experimental results outperformed state-of-the-art methods

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