SIFT, BoW Architecture and one-against-all Support Vector Machine

Abstract. For this first participation to ImageClef Plant Identification, we build on the reference Bag-of-Word framework (BoW). We extract Points-of-Interest (PoI) using the SIFT detector in every image and describe each local feature with the SIFT descriptor. The visual dictionary is built with a K-means algorithm of 100 clusters on the local features. Each image is then represented by its histogram onto the dictionary using hard-assignment strategy. We classify the images with as many binary one-against-all Support Vector Machines as the number of plant classes per organ types. Our aim is to evaluate for the plant identification task a classic baseline of multi-class image categorization. Our first results illustrate how difficult this task is and that a framework which has become a standard baseline for classifying general image datasets is not immediately relevant on Plant Identification data.