NDLI: An Adaptive Recognition Model for Image Annotation

Content Provider	IEEE Xplore Digital Library
Author	Zenghai Chen Hong Fu Zheru Chi Feng, D.D.
Copyright Year	1998
Abstract	In this paper, an adaptive recognition model (ARM) is proposed for image annotation. The ARM consists of an adaptive classification network (CFN) and a nonlinear correlation network (CLN). The adaptive CFN aims to annotate an image with keywords, and the CLN is used to unveil the correlative information of keywords for annotation refinement. Image annotation is carried out by an ARM in two stages. In the first stage, the features extracted from regions of the input image are fed to a CFN to produce classification labels. In the second stage, the CLN uses keyword correlations learned from the training images to refine the classification result. The ARM works in a forward-propagating manner, resulting in high efficiency in image annotation. Furthermore, the computational time of an ARM is insensitive to the number of regions of the input image and the vocabulary size. In this paper, the effect of keyword correlation in image annotation is, comprehensively, investigated on a real image dataset and a synthetic image dataset. The exploitation of a controllable synthetic dataset helps to systematically study the function of keyword correlation and effectively analyze the performance of the ARM. Experimental results demonstrate the efficiency and effectiveness of the ARM.
Starting Page	1120
Ending Page	1127
Page Count	8
File Size	610640
File Format	PDF
ISSN	10946977
Volume Number	42
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Image segmentation Image color analysis Feature extraction synthetic image dataset Adaptive recognition model (ARM) image annotation keyword correlation neural networks
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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