Image annotation by large-scale content-based image retrieval

2006 ◽  
Author(s):  
Xirong Li ◽  
Le Chen ◽  
Lei Zhang ◽  
Fuzong Lin ◽  
Wei-Ying Ma
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Image Annotation ◽  
Content Based Image Retrieval

scholarly journals Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods

2021 ◽  
Vol 10 (11) ◽  
pp. 748
Author(s):  
Ferdinand Maiwald ◽  
Christoph Lehmann ◽  
Taras Lazariv
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Image Retrieval ◽  
Pose Estimation ◽  
Large Scale ◽  
Feature Matching ◽  
Geographic Information ◽  
Content Based Image Retrieval ◽  
Matching Methods ◽  
Historical Images

The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models.

scholarly journals Towards Large Scale Image Retrieval System Using Parallel Frameworks

2020 ◽  
Author(s):  
Saliha Mezzoudj
Keyword(s):  
Big Data ◽  
Image Retrieval ◽  
Large Scale ◽  
Retrieval System ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Content Search ◽  
Parallel Frameworks ◽  
Image Retrieval System ◽  
Large Scale Image Retrieval

Recently, the increasing use of mobile devices, such as cameras and smartphones, has resulted in a dramatic increase in the amount of images collected every day. Therefore, retrieving and managing these large volumes of images has become a major challenge in the field of computer vision. One of the solutions for efficiently managing image databases is an Image Content Search (CBIR) system. For this, we introduce in this chapter some fundamental theories of content-based image retrieval for large scale databases using Parallel frameworks. Section 2 and Section 3 presents the basic methods of content-based image retrieval. Then, as the emphasis of this chapter, we introduce in Section 1.2 A content-based image retrieval system for large-scale images databases. After that, we briefly address Big Data, Big Data processing platforms for large scale image retrieval. In Sections 5, 6, 7, and 8. Finally, we draw a conclusion in Section 9.

A Multi-resolution Approach for Content-based Image Retrieval on the Grid

2005 ◽  
Vol 44 (02) ◽  
pp. 211-214 ◽  
Author(s):  
T. Tweed ◽  
S. Miguet ◽  
K. Hassan
Keyword(s):  
Image Retrieval ◽  
High Performance ◽  
Large Scale ◽  
Breast Cancer Diagnosis ◽  
Content Based Image Retrieval ◽  
Distributed Data ◽  
Promising Tool ◽  
Medical Centers ◽  
Image Retrieval System ◽  
Processing Resources

Summary Objectives: Hospitals and medical centers are producing more and more data that need to be processed. Those data are confidential, heterogeneous, and limited to the geographic site where they have been produced. Unless properly anonymized, they cannot be distributed on wide area networks. Methods: Grid technologies allow the globalization of storage and processing resources, and enable large-scale experimentations on distributed data. They constitute a promising tool to treat the different data and analyze the knowledge they contain, while offering secured access and high-performance computing capacities to the different users. Our aim is to evaluate the possibilities of grid technologies for handling medical data. Results and Conclusions: In this paper, we focus on a breast cancer diagnosis assistance tool, based on distributed and incremental knowledge construction and a content-based image retrieval system. We analyze the different scenarios of uses of such a tool. We further propose an algorithm that indexes mammographic images for content-based query purposes. This algorithm is tested on images of different resolutions in order to reduce the indexation time and we analyze its performance with experiments on the grid.

Content-Based Image Retrieval for Medical Image Analysis

Data Mining ◽  
2013 ◽  
pp. 1097-1113
Author(s):  
Jianhua Yao ◽  
Ronald M. Summers
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Annotation ◽  
Database Systems ◽  
Research Field ◽  
Content Based Image Retrieval ◽  
Image Management ◽  
Interaction Database ◽  
Active Research ◽  
Main Components

The growing repositories of clinical imaging data generate a need for effective image management and access that demands more than simple text-based queries. Content-Based Image Retrieval (CBIR) is an active research field and has drawn attention in recent years. It is a technique to organize and search image archives by their visual content. It is a multi-discipline field that integrates technologies from computer vision, machine learning, information retrieval, human-machine interaction, database systems, and data mining. CBIR consists of four main components: database and indexing, feature extraction, query formation and interface, and similarity measures. The applications of CBIR to the medical field include PACS integration, image annotation/codification, computer-aided diagnosis, case-based reasoning, and teaching tools. This chapter intends to disseminate the CBIR techniques to their applications to medical image management and analysis and to attract greater interest from various research communities to advance research in this field.

scholarly journals Large Scale Near-Duplicate Celebrity Web Images Retrieval Using Visual and Textual Features

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Fengcai Qiao ◽  
Cheng Wang ◽  
Xin Zhang ◽  
Hui Wang
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Image Annotation ◽  
Research Problem ◽  
Visual Features ◽  
Query Image ◽  
Visual Words ◽  
Historical Figures ◽  
Web Images ◽  
Textual Features

Near-duplicate image retrieval is a classical research problem in computer vision toward many applications such as image annotation and content-based image retrieval. On the web, near-duplication is more prevalent in queries for celebrities and historical figures which are of particular interest to the end users. Existing methods such as bag-of-visual-words (BoVW) solve this problem mainly by exploiting purely visual features. To overcome this limitation, this paper proposes a novel text-based data-driven reranking framework, which utilizes textual features and is combined with state-of-art BoVW schemes. Under this framework, the input of the retrieval procedure is still only a query image. To verify the proposed approach, a dataset of 2 million images of 1089 different celebrities together with their accompanying texts is constructed. In addition, we comprehensively analyze the different categories of near duplication observed in our constructed dataset. Experimental results on this dataset show that the proposed framework can achieve higher mean average precision (mAP) with an improvement of 21% on average in comparison with the approaches based only on visual features, while does not notably prolong the retrieval time.

Content-Based Image Retrieval for Medical Image Analysis

2012 ◽  
pp. 202-219
Author(s):  
Jianhua Yao ◽  
Ronald M. Summers
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Annotation ◽  
Database Systems ◽  
Research Field ◽  
Content Based Image Retrieval ◽  
Image Management ◽  
Interaction Database ◽  
Active Research ◽  
Main Components

The growing repositories of clinical imaging data generate a need for effective image management and access that demands more than simple text-based queries. Content-Based Image Retrieval (CBIR) is an active research field and has drawn attention in recent years. It is a technique to organize and search image archives by their visual content. It is a multi-discipline field that integrates technologies from computer vision, machine learning, information retrieval, human-machine interaction, database systems, and data mining. CBIR consists of four main components: database and indexing, feature extraction, query formation and interface, and similarity measures. The applications of CBIR to the medical field include PACS integration, image annotation/codification, computer-aided diagnosis, case-based reasoning, and teaching tools. This chapter intends to disseminate the CBIR techniques to their applications to medical image management and analysis and to attract greater interest from various research communities to advance research in this field.

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