{'fraction_non_alphanumeric': 0.02080624187256177, 'fraction_numerical': 0.009102730819245773, 'mean_word_length': 5.205645161290323, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Floristic participation at LifeCLEF 2016 Plant Identification Task

This paper describes the participation of the Floristic consortium to the LifeCLEF 2016 plant identification challenge[18]. The aim of the task was to produce a list of relevant species for a large set of plant images related to 1000 species of trees, herbs and ferns living in Western Europe, knowing that some of these images belonged to unseen categories in the training set like plant species from other areas, horticultural plants or even off topic images (people, keyboards, animals, etc). To address this challenge, we first experimented as a baseline, without any rejection procedure, a Convolutional Neural Network (CNN) approach based on a slightly modified GoogLeNet model. In a second run, we applied a simple rejection criteria based on probability threshold estimation on the output of the CNN, one for each species, for removing automatically 
species propositions judged irrelevant. In the third run, rather than definitely eliminating some species predictions with the risk to remove false 
negative propositions, we applied various attenuation factors in order to revise the probability distributions given by the CNN as confident score 
expressing how much a query was related or not to the known species. 
More precisely, for this last run we used the geographical information and several cohesion measures in terms of observation, "organ" tags and 
taxonomy (genus and family levels) based on a knn similarity search results within the training set.