Sabanci-okan system at image Clef 2012: Combining features and classifiers for plant identification

Abstract

We describe our participation in the plant identification task of ImageClef 2012. We submitted two runs, one fully automatic and another one where human assistance was provided for the images in the photo category. We have not used the meta-data in either one of the systems, for exploring the extent of image analysis for the plant identification problem. Our approach in both runs employs a variety of shape, texture and color descriptors (117 in total). We have found shape to be very discriminative for isolated leaves (scan and pseudoscan categories), followed by texture. While we have experimented with color, we could not make use of the color information. We have employed the watershed algorithm for segmentation, in slightly different forms for automatic and human assisted systems. Our systems have obtained the best overall results in both automatic and manual categories, with 43% and 45% identification accuracies respectively. We have also obtained the best results on the scanned image category with 58% accuracy.