Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/43736
Title: Improving digital image retrieval towards image understanding and organization
Authors: CHEN QI
Keywords: Image Retrieval, Image Annotation, Fashion Understanding, Image Clustering, Crowdsourcing, Active Learning
Issue Date: 21-May-2013
Source: CHEN QI (2013-05-21). Improving digital image retrieval towards image understanding and organization. ScholarBank@NUS Repository.
Abstract: Image retrieval is to perform image browsing, searching and retrieving through a large digital database. Current image retrieval systems usually suffer from noisy images. Understanding the content of images in an effective and efficient manner is very necessary and thus becomes one of the research topics in this dissertation. Another problem investigated in this dissertation is image search result organization. Most image retrieval systems display search results in a flat structure which is not convenient. In terms of image content understanding, we make one step ahead to automatically associate images with semantic-related keywords, which is called automatic image annotation. We also consider image annotation in a specific domain. We investigate how to understand fashion since which has become a very large industrial sector around the world. On the topic of image search result organization, we aim to utilize clustering techniques to facilitate image searching and browsing.
URI: http://scholarbank.nus.edu.sg/handle/10635/43736
Appears in Collections:Ph.D Theses (Open)

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