DUTh at the ImageCLEF 2016 Image Annotation Task: Content Selection

This report describes our experiments in the Content Selection subtask of the Image Annotation task of ImageClef 2016[7, 13]. Our approach is based on the fact that the human visual system concentrates mostly on local features [12]. In this respect, we trained an SVM classifier with descriptors that are based on the local features of the image, such as edges and corners. For the experimentation process we used the set of 500 images provided for the task, divided into training and test set. This set was particularly created for this year’s new subtask, Content Selection, although the concepts are the same as last year. Through experimentation we determine which descriptors give the best results for the given task. To conduct the main experiment the SVM classifier is trained with the aforementioned set of 500 images using a subset of the top-performing features. Consecutively, the SVM processes the new set of 450 images and selects the boxes that best describe them conceptually.