Identification of plant species on large botanical image datasets

Keywords: LifeCLEF, plant, leaves, leaf, flower, fruit, bark, stem, branch, species, retrieval, images, collection, identification, fine-grained classifi- cation,

For a query image I q , according to its organ/view, the basic algorithm applied is: (i) starting from the retrieved lists of similar training images for each type of feature,

The challenge of our second method is to identify plant species based on combination of textual and structural context of image.. Indeed, we have used the meta-data in our system

Advanced shape context for plant species identification using leaf image retrieval.. Sofiène Mouine, Itheri Yahiaoui,

Our goal was to synthesize available data on vascular plant species distribution across sub tropical Africa in a single and comprehensive database in order to (1)

Although LifeCLEF Plant identification task is about more fine-grained image clas- sification compared to ImageNet’s general object category classification, finetuning

To evaluate to what extent DCNNs can learn from noisy data compared to trusted data, some models are trained using only images of the Encyclopedia of Life dataset

Step 3: Initializing Species Layers After we obtained the organ layers, we train the species layers based on the species labeled dataset as shown in