Abstract:
Habitat classification is an important ecological activity used to monitor environmental biodiversity. Current classification techniques rely heavily on human surveyors a...Show MoreMetadata
Abstract:
Habitat classification is an important ecological activity used to monitor environmental biodiversity. Current classification techniques rely heavily on human surveyors and are laborious, time consuming, expensive and subjective. In this paper, we approach habitat classification as an automatic image annotation problem. We have developed a novel method for annotating ground-taken photographs with the habitats present in them using random projection forests. For this purpose, we have collected and manually annotated a geo-referenced habitat image database with over 1000 ground photographs. We compare the use of two different types of input (blocks within images and the whole images) to classify habitats. We also compare our approach with a popular random forest implementation. Results show that our approach has a lower error rate and it is able to classify three habitats (Woodland and scrub, Grassland and marsh, and Miscellaneous) with a high recall.
Published in: 2013 IEEE International Conference on Image Processing
Date of Conference: 15-18 September 2013
Date Added to IEEE Xplore: 13 February 2014
Electronic ISBN:978-1-4799-2341-0
ISSN Information:
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- Index Terms
- Random Forest ,
- Image Annotation ,
- Habitat Categories ,
- Grassland ,
- High Recall ,
- Random Projection ,
- Random Forest Implementation ,
- Different Types Of Inputs ,
- Decision Tree ,
- Codebook ,
- First Category ,
- Precision And Recall ,
- Habitat Types ,
- Optimal Threshold ,
- Aerial Images ,
- Random Vector ,
- Satellite Imagery ,
- Forest Trees ,
- Image Patches ,
- Leaf Node ,
- Node Splitting ,
- Color Pattern ,
- Binary Decision Tree ,
- Final Distribution ,
- Codeword ,
- Split Function
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- Index Terms
- Random Forest ,
- Image Annotation ,
- Habitat Categories ,
- Grassland ,
- High Recall ,
- Random Projection ,
- Random Forest Implementation ,
- Different Types Of Inputs ,
- Decision Tree ,
- Codebook ,
- First Category ,
- Precision And Recall ,
- Habitat Types ,
- Optimal Threshold ,
- Aerial Images ,
- Random Vector ,
- Satellite Imagery ,
- Forest Trees ,
- Image Patches ,
- Leaf Node ,
- Node Splitting ,
- Color Pattern ,
- Binary Decision Tree ,
- Final Distribution ,
- Codeword ,
- Split Function
- Author Keywords