Participation of LSIS/DYNI to ImageCLEF 2012 Plant Images Classification Task

This paper presents the participation of the LSIS/DYNI team for the ImageCLEF 2012 plant identication challenge. Image- CLEF's plant identication task provides a testbed for the system-oriented evaluation of tree species identication based on leaf images. The goal is to investigate image retrieval approaches in the context of crowd sourced images of leaves collected in a collaborative manner. The LSIS/DYNI team submitted three runs to this task and obtained the best evalua- tion scores (S = 0:32) for the "photograph" image category with an automatic method. Our approach is based on a modern computer vi- sion framework involving local, highly discriminative visual descriptors, sophisticated visual-patches encoder and large-scale supervised classi- cation. The paper presents the three procedures employed, and provides an analysis of the obtained evaluation results.

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