Automatically assigned DDC number: 006312

Manually assigned DDC number: 006312

Number of references: 16

Title: Automatic Selection of Visual Features and Classifiers

Author:

Author:

Subject: Ro Jaimes,Shih-fu Chang Automatic Selection of Visual Features and Classifiers

Description: In this paper, we propose a dynamic approach to feature and classifier selection. In our approach, based on performance, visual features and classifiers are selected automatically. In earlier work, we presented the Visual Apprentice, in which users can define visual object models via a multiple-level object definition hierarchy (region, perceptual-area, object-part, and object). Visual Object Detectors are learned, using various learning algorithms- as the user provides examples from images or video, visual features are extracted and multiple classifiers are learned for each node of the hierarchy. In this paper, features and classifiers are selected automatically at each node, depending on their performance over the training set provided by the user. Thus, changes in the training data yield dynamic changes in the features and classifiers used. We introduce the concept of Recurrent Visual Semantics and show how it can be used to identify domains in which performancebased learning techni...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 2000-03-22

Pubyear: 2000

Format: ps

Identifier: http://citeseer.ist.psu.edu/334520.html

Source: http://www.ctr.columbia.edu/~ajaimes/Pubs/spie00_storage.ps

Language: en

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