NDLI: A New Method for Image Classification by Using Multilevel Association Rules

Content Provider	IEEE Xplore Digital Library
Author	Tseng, V.S. Ming-Hsiang Wang Ja-Hwung Su
Copyright Year	2005
Description	Author affiliation: National Cheng Kung University, Taiwan (Tseng, V.S.)
Abstract	With the popularity of multimedia applications, the huge amount of image and video related to real life have led to the proliferation of emerging storage techniques. Contented-based image retrieval and classification have become attractive issues in the last few years. Most researches concerning image classification focus primarily on low-level image features (e.g. color, texture, shape, etc.) and ignore the conceptual associations among the objects in the images. In this paper, we propose a new image classification method by using multiple-level association rules based on the image objects. The approach we proposed can be decomposed of three phases: (1) building of conceptual object hierarchy, (2) discovery of classification rules, and (3) classification and prediction of images. At the first phase, we use a hierarchical clustering method to build the conceptual hierarchy based on the low-level features of image objects. At the second phase, we devise a multi-level mining algorithm for finding the image classification rules. The classification task is performed at the last phase. Empirical evaluations show that our approach performs better than other approaches in terms of classification accuracy.
Starting Page	1180
Ending Page	1180
File Size	238819
Page Count	1
File Format	PDF
ISBN	0769526578
DOI	10.1109/ICDE.2005.164
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-04-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Content based retrieval Image retrieval Multimedia databases Information retrieval Feature extraction Association rules Data mining Image storage Image classification
Content Type	Text
Resource Type	Article

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