2026-09-19T07:43:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/505052021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tomás Lozano-Pérez.
Yang, Cheng, 1974-
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-01-07T20:46:51Z
2010-01-07T20:46:51Z
1998
1998
http://hdl.handle.net/1721.1/50505
42306274
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
Includes bibliographical references (p. 81-82).
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.
by Cheng Yang.
S.M.
82 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science
Image database retrieval with multiple-instance learning techniques
Thesis
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