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Selecting features for object detection using an AdaBoost-compatible evaluation function
Fürst, Luka (Author), Fidler, Sanja (Author), Leonardis, Aleš (Author)

URLURL - Presentation file, Visit http://www.sciencedirect.com/science/journal/01678655 This link opens in a new window

Abstract
This paper addresses the problem of selecting features in a visual object detection setup where a detection algorithm is applied to an input image represented by a set of features. The set of features to be employed in the test stage is prepared in two training-stage steps. In the first step, a feature extraction algorithm produces a (possibly large) initial set of features. In the second step, on which this paper focuses, the initial set is reduced using a selection procedure. The proposed selection procedure is based on a novel evaluation function that measures the utility of individual features for a certain detection task. Owing to its design, the evaluation function can be seamlessly embedded into an AdaBoost selection framework. The developed selection procedure is integrated with state-of-the-art feature extraction and object detection methods. The presented system was tested on five challenging detection setups. In three of them, a fairly high detection accuracy was effected by as few as six features selected out of several hundred initial candidates.

Language:English
Keywords:razpoznavanje vzorcev, računalniški vid, izbira značilnic, algoritem AdaBoost, detekcija objektov
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:FRI - Faculty of computer and information science
Year:2008
Publisher:Elsevier Science Inc.
Number of pages:str. 1603-1612
Numbering:Vol. 29, no. 11
UDC:004.93'1
ISSN on article:0167-8655
COBISS.SI-ID:6494804 Link is opened in a new window
Views:809
Downloads:263
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Record is a part of a journal

Title:Pattern recognition letters
Shortened title:Pattern recogn. lett.
Publisher:North-Holland
ISSN:0167-8655
COBISS.SI-ID:26103296 This link opens in a new window

Secondary language

Language:Unknown
Keywords:pattern recognition, computer vision, feature selection, AdaBoost algorithm, object detection

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