Image database retrieval with multiple-instance learning techniques
Year: 2002
Type: conference-paper
Abstract: In this paper, we develop and test an approach for 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 medium-sized database of natural scenes as well as single- and multiple-object images.
Authors: C. Yang, T. Lozano-Perez
Institutions: Massachusetts Institute of Technology, Stanford University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Office of Naval Research
OpenAlex ID: W2152195571