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ICDE
2000
IEEE

Image Database Retrieval with Multiple-Instance Learning Techniques

14 years 11 months ago
Image Database Retrieval with Multiple-Instance Learning Techniques
In this paper, 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 medium-sized database of natural scenes as well as single- and multiple-object images.
Cheng Yang, Tomás Lozano-Pérez
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2000
Where ICDE
Authors Cheng Yang, Tomás Lozano-Pérez
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