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|Title:||Image annotation by search||Authors:||Luong, Phuoc Thanh.||Keywords:||DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval||Issue Date:||2011||Abstract:||The recent advances in technology have led to an exponential growth in the number of digital images being stored on the Internet as well as in personal computers. Effective methods to organize and index photos based on semantic content have become essential to provide users with the convenience of searching their albums for specific content without prior manual annotation. However, querying for the image content is still a challenging task which has attracted much research effort. In this paper, we present a photo query framework based on prior annotation. When the user provides a text query (e.g. “water”), the framework performs a search within the annotation database and finds relevant photos. To accomplish this goal, we built a set of classifiers to annotate user photos in advance, and used these annotations for query. We leveraged the NUS-WIDE dataset, which contains publicly available web images and their associated labels, to train the classifiers. These classifiers are used to detect the presence of concepts in each photo in a photo folder, and annotate the photos with suitable labels. To increase the accuracy of the annotation process, we conducted experiments on two simple but effective classification methods, k Nearest Neighbor (kNN) and Support Vector Machine (SVM), and determine the best method by considering their accuracy and speed.||URI:||http://hdl.handle.net/10356/44873||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
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