Image database retrieval with multiple-instance learning techniques
Name
42306274-MIT.pdf
Description
Full printable version
Size
9.12 MB
Format
Adobe PDF
Checksum (MD5)
e02ea2801f1f120b7b9bd7268578daa4
Author(s)
Yang, Cheng, 1974-
Advisor(s)
Tomás Lozano-Pérez.
Date Issued
1998
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we develop and test an approach to retrieving images from an image database based on content similarity. First, each picture is divided into many overlapping regions. For each region, the sub-picture is filtered and converted into a feature vector. In this way, each picture is represented by a number of different feature vectors. The user selects positive and negative image examples to train the system. During the training, a multiple-instance learning method known as the Diverse Density algorithm is employed to determine which feature vector in each image best represents the user's concept, and which dimensions of the feature vectors are important. The system tries to retrieve images with similar feature vectors from the remainder of the database. A variation of the weighted correlation statistic is used to determine image similarity. The approach is tested on a large database of natural scenes as well as single- and multiple-object images. Comparisons are made against a previous approach, and the effects of tuning various training parameters, as well as that of adjusting algorithmic details, are also studied.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
Includes bibliographical references (p. 81-82).
Subjects
Electrical Engineering and Computer Science
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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