MICA at ImageClef 2013 Plant Identification Task Thi-Lan LE, Ngoc-Hai PHAM International Research Institute MICA – UMI2954 – HUST Thi-Lan.LE@mica.edu.vn , Ngoc-Hai.Pham@mica.edu.vn I. Introduction In the framework of ImageClef 2013 [1], plant identification task [2], we have submitted three runs. For the first run named Mica Run 1, we employ GIST descriptor with k-nearest neighbor (kNN) for all sub-categories. Concerning Mica Run 2, we observe that global descriptors such as color histogram and texture are able to distinguish classes of two sub-categories that are flower and entire. For the others sub-categories, we still employ GIST descriptor and kNN. Based on our work for leaf identification, for the third run (named MICA run 3), we have proposed to apply our method for leaf images for both SheetasBackground and Natural background. For the remaining subcategories, we used the same method as the two first runs. Concerning our method for leaf images, we firstly apply Un-sharp Masking (USM) on SheetAsBackground images. Then, we extract Speeded-Up Robust Features (SURF). Finally, we used Bag-of-Words (BoW) for calculating feature vector of each image and Support Vector Machine (SVM) for training the model of each class in training dataset and for predicting class id of new images. In this paper, we describe in detail the algorithms used in our runs. II. Our plant identification methods 1. Plant identification method of MICA run 1 Results of variety of state of the art scene recognition algorithms [3] shown that GIST features 1 [11] obtains an acceptable result of outdoor scene classification (appr. 73 – 80 %). Therefore, in this study, we would like to investigate if GIST features are still good for plant identification. In this section, we briefly describe procedures of GIST feature extractions proposed in [4]. To capture remarkable/considering of a scene, Oliva et al in [4] evaluated sev- en characteristics of a outdoor scenes such as naturalness, openness, roughness, 1 Gist feature present a brief observation or a report at the first glance of a outdoor scene that summarizes the quintessential characteristics of an image