Dottorato di Ricerca in
Ingegneria dell'Informazione
Recognition and classification of visual context
Abstract
Automatic image annotation is an important task, in which the goal is to determine the relevance of annotation terms for images. Several efforts have been made in recent years to design and develop effective and efficient algorithms for visual recognition and retrieval.
To this end, a common and successful approach is to quantize local visual features (e.g. SIFT) following the well-known bag-of-visual-words paradigm. Then, a classifier (e.g. SVM) can be learned from a collection of images manually labeled as belonging to an object category or not. The goal of this tutorial is to get theoretical understanding and practical experience with image classification.
The participants will learn the fundamentals of image classification and will be guided to implement a system in Matlab based on bag-of-visual-words image representation and will apply it to image classification. The emphasis of the tutorial will be on the important general concepts rather than in depth coverage of contemporary papers.