scholarly journals Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation

2016 ◽  
Author(s):  
Hoo-Chang Shin ◽  
Kirk Roberts ◽  
Le Lu ◽  
Dina Demner-Fushman ◽  
Jianhua Yao ◽  
...  
Keyword(s):  
Image Annotation ◽  
Cascade Model ◽  
X Rays ◽  
Learning To Read ◽  
Automated Image Annotation

Show&Tell: a semi-automated image annotation system

2000 ◽  
Vol 7 (3) ◽  
pp. 61-71 ◽  
Author(s):  
R.K. Srihari ◽  
Z. Zhang
Keyword(s):  
Image Annotation ◽  
Annotation System ◽  
Automated Image Annotation

Exploiting Term Co-occurrence for Enhancing Automated Image Annotation

2009 ◽  
pp. 632-639 ◽  
Author(s):  
Ainhoa Llorente ◽  
Simon Overell ◽  
Haiming Liu ◽  
Rui Hu ◽  
Adam Rae ◽  
...  
Keyword(s):  
Image Annotation ◽  
Automated Image Annotation

Probability Association Approach in Automatic Image Annotation

2008 ◽  
pp. 615-626
Author(s):  
Feng Xu ◽  
Yu-Jin Zhang
Keyword(s):  
Large Scale ◽  
Image Annotation ◽  
Public Life ◽  
Image Database ◽  
Web Pages ◽  
Challenging Problem ◽  
Semantic Concepts ◽  
Automated Image Annotation ◽  
High Level ◽  
The Web

Content-based image retrieval (CBIR) has wide applications in public life. Either from a static image database or from the Web, one can search for a specific image, generally browse to make an interactive choice, and search for a picture to go with a broad story or to illustrate a document. Although CBIR has been well studied, it is still a challenging problem to search for images from a large image database because of the well-acknowledged semantic gap between low-level features and high-level semantic concepts. An alternative solution is to use keyword-based approaches, which usually associate images with keywords by either manually labeling or automatically extracting surrounding text from Web pages. Although such a solution is widely adopted by most existing commercial image search engines, it is not perfect. First, manual annotation, though precise, is expensive and difficult to extend to large-scale databases. Second, automatically extracted surrounding text might by incomplete and ambiguous in describing images, and even more, surrounding text may not be available in some applications. To overcome these problems, automated image annotation is considered as a promising approach in understanding and describing the content of images.

scholarly journals Review: Automatic Semantic Image Annotation

2016 ◽  
Vol 15 (12) ◽  
pp. 7290-7297
Author(s):  
Shereen A. Hussein ◽  
Howida Youssry Abd El Naby ◽  
Aliaa A. A. Youssif
Keyword(s):  
Digital Images ◽  
Image Annotation ◽  
Critical Issue ◽  
Image Features ◽  
Intermediate Step ◽  
Digital Cameras ◽  
Storage Devices ◽  
Automated Image Annotation ◽  
And Storage ◽  
Semantic Image Annotation

There are many approaches for automatic annotation in digital images. Nowadays digital photography is a common technology for capturing and archiving images because of the digital cameras and storage devices reasonable price. As amount of the digital images increase, the problem of annotating a specific image becomes a critical issue. Automated image annotation is creating a model capable of assigning terms to an image in order to describe its content. There are many image annotation techniques that seek to find the correlation between words and image features such as color, shape, and texture to provide an automatically correct annotation words to images which provides an alternative to the time consuming work of manual image annotation. This paper aims to cover a review on different Models (MT, CRM, CSD-Prop, SVD-COS and CSD-SVD) for automating the process of image annotation as an intermediate step in image retrieval process using Corel 5k images.

Custom Ontologies for an Automated Image Annotation System

2011 ◽  
pp. 505-515
Author(s):  
Gabriel Mihai ◽  
Liana Stanescu ◽  
Dumitru Dan Burdescu ◽  
Marius Brezovan
Keyword(s):  
Image Annotation ◽  
Annotation System ◽  
Automated Image Annotation
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