NDLI: Grass, scrub, trees and random forest

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Grass, scrub, trees and random forest

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Grass, scrub, trees and random forest

Content Provider	ACM Digital Library
Author	Qiu, Guoping Torres, Mercedes
Abstract	Habitat classification is important for monitoring the environment and biodiversity. Currently, this is done manually by human surveyors, a laborious, expensive and subjective process. We have developed a new computer habitat classification method based on automatically tagging geo-referenced ground photographs. In this paper, we present a geo-referenced habitat image database containing over 400 high-resolution ground photographs that have been manually annotated by experts based on a hierarchical habitat classification scheme widely used by ecologists. This will be the first publicly available image database specifically designed for the development of multimedia analysis techniques for ecological (habitat classification) applications. We formulate photograph-based habitat classification as an automatic image tagging problem and we have developed a novel random-forest based method for annotating an image with the habitat categories it contains. We have also developed an efficient and fast random-projection based technique for constructing the random forest. We present experimental results to show that ground-taken photographs are a potential source of information that can be exploited in automatic habitat classification and that our approach is able to classify with a reasonable degree of confidence three of the main habitat classes: Woodland and Scrub, Grassland and Marsh and Miscellaneous.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450315883
DOI	10.1145/2390832.2390834
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-11-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ground-taken photography Habitat classification Image processing Image annotation Random forests
Content Type	Text
Resource Type	Article

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