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Integrating Multiple Classifiers In Visual Object Detectors Learned From User Input (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Jaimes, Alejandro Chang, Shih-Fu |
| Description | There have been many recent efforts in contentbased retrieval to perform automatic classification of images/visual objects. Most approaches, however, have focused on using individual classifiers. In this paper, we study the way in which, in a dynamic framework, multiple classifiers can be combined when applying Visual Object Detectors. We propose a hybrid classifier combination approach, in which decisions of individual classifiers are combined in the following three ways: (1) classifier fusion, (2) classifier cooperation, and (3) hierarchical combination. In earlier work, we presented the Visual Apprentice framework, in which a user defines visual object models via a multiple-level object-definition hierarchy (region, perceptual-area, object part, and object). As the user provides examples from images or videos, visual features are extracted and multiple classifiers are learned for each node of the hierarchy. In this paper, we discuss the benefits of hybrid classifi... |
| File Format | |
| Language | English |
| Publisher Date | 2000-01-01 |
| Publisher Institution | INVITED PAPER, SESSION ON IMAGE AND VIDEO DATABASES, 4TH ASIAN CONFERENCE ON COMPUTER VISION (ACCV 2000 |
| Access Restriction | Open |
| Subject Keyword | Hybrid Classifi Multiple Classifier User Input Multiple-level Object-definition Hierarchy Many Recent Effort Image Visual Object Contentbased Retrieval Hierarchical Combination Visual Apprentice Framework Visual Object Detector Visual Object Model Visual Feature Hybrid Classifier Combination Approach Object Part Dynamic Framework Automatic Classification Individual Classifier |
| Content Type | Text |
| Resource Type | Article |
