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  1. Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data (MAED '12)
  2. Grass, scrub, trees and random forest
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Grass, scrub, trees and random forest
Identification of great apes using gabor features and locality preserving projections
Plant leaves morphological categorization with shared nearest neighbours clustering
Visibility cameras: where and how to look
Texture recognition for frog identification
Multi-organ plant identification
Environmental data extraction from multimedia resources
Event detection in underwater domain by exploiting fish trajectory clustering
Semantic based retrieval system of arctic animal images
An environmental search engine based on interactive visual classification
A visual sensing platform for creating a smarter multi-modal marine monitoring network
Quantitative performance analysis of object detection algorithms on underwater video footage

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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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