NDLI: A combined hierarchical model for automatic image annotation and retrieval

Content Provider	IEEE Xplore Digital Library
Author	Sumathi, T. Hemalatha, M.
Copyright Year	2011
Description	Author affiliation: Karpagam University, Coimbatore. Tamil nadu, India (Sumathi, T.) \|\| Department of software systems, Karpagam University, Coimbatore, tamil nadu, India (Hemalatha, M.)
Abstract	Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision technique is used in image retrieval system to organize and locate images of interest from a database. Many techniques have been proposed for image annotation in the last decade that gives reasonable performance on standard datasets. In this work, we introduce an innovative hybrid model for image annotation that treats annotation as a retrieval problem. The proposed technique utilizes low level image features and a simple combination of basic distances using JEC to find the nearest neighbors of a given image; the keywords are then assigned using SVM approach which aims to explore the combination of three different methods. First, the initial annotation of the data using two known methods, and that takes the hierarchy into consideration by classifying consecutively its instances; finally, we make use of pair wise majority voting between methods by simply summing strings in order to produce a final annotation. The proposed technique results show that this method outperforms the current state of art methods on the standard datasets.
Starting Page	135
Ending Page	139
File Size	106203
Page Count	5
File Format	PDF
ISBN	9781467306706
e-ISBN	9781467306713
DOI	10.1109/ICoAC.2011.6165162
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-14
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Radial basis function Image color analysis Image retrieval Image annotation Feature extraction Educational institutions JEC SVM Kernel
Content Type	Text
Resource Type	Article

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