Maestra at LifeCLEF 2014 Plant Task: Plant Identification using Visual Data

In this paper, we describe an approach to the automatic plant identification task of the LifeCLEF 2014 lab. The image descriptors for all submitted runs were obtained using the bag-of-visual-words method. For the leaf scans, we use multiscale triangular shape descriptor and for the other plant organs Opponent SIFT extracted around points of interest obtained using Harris-Laplace detector. We then use approximate k-means (AKM) algorithm to cluster these descriptors in large number of clusters/visual words (approximately 200K). Each image in the training and test dataset is represented as a sparse high-dimensional histogram of term (visual word) occurrences. The similarity between two images is defined as a L2 distance over the obtained histograms. We use the standard tf-idf weighting scheme, which reduces the contribution that commonly occurring, and therefore less discriminative, words make to the similarity. To obtain the predictions, we employ a late fusion scheme for combining the similarities/ranks from multiple ranked image lists build for each type of view. Overall the proposed methods performed well, we ranked fifth, out of 10 competing groups.