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  1. Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data (MAED '12)
  2. Plant leaves morphological categorization with shared nearest neighbours clustering
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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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Plant leaves morphological categorization with shared nearest neighbours clustering

Content Provider ACM Digital Library
Author Joly, Alexis Hamzaoui, Amel Goëau, Hervé
Abstract This paper presents an original experiment aimed at evaluating if state-of-the-art visual clustering techniques are able to automatically recover morphological classifications built by the botanists themselves. The clustering phase is based on a recent Shared-Nearest Neighbours (SNN) clustering algorithm, which allows to combine effectively heterogeneous visual information sources at the category level. Each resulting cluster is associated with an optimal selection of visual similarities, allowing to discover diverse and meaningful morphological categories even if we use a blind set of visual sources as input. Experiments are performed on ImageCLEF 2011 plant identification dataset, that was specifically enriched in this work with morphological attributes tags (annotated by expert botanists). The results are very promising, since all clusters discovered automatically can be easily matched to one node of the morphological tree built by the botanists.
Starting Page 37
Ending Page 40
Page Count 4
File Format PDF
ISBN 9781450315883
DOI 10.1145/2390832.2390842
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-11-02
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Multisource Shared nearest neighbours clustering Botanic
Content Type Text
Resource Type Article
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