Çizim tanıma için aktif öğrenme ve aktif sahne öğrenimi

Sketching is a natural and effective means for expressing and sharing ideas. These qualities have made sketching an emerging interaction modality in pen-based systems. Sketch-based interfaces rely on the availability of accurate sketch recognition engines, which in turn require large amounts of labeled data for training. Unfortunately, labeling sketch data is time consuming and expensive, because it requires the involvement of human annotators. We demonstrate the utility of the active learning technology in reducing the amount of manual annotation required to achieve target recognition accuracy. The first part of our work presents the first comprehensive study on the use of active learning for isolated sketch recognition. We present results from an extensive analysis which shows that the utility of active learning depends on a number of practical factors that require careful consideration. These factors include the choices of batch selection strategies, informativeness measures, seed set size, and domain-specific factors such as feature representation and the choice of database. Since active learning community lacks such factor based analysis, our empirical analysis is examplary. Our results imply that the Margin-based informativeness measure consistently outperforms other measures. We also show that the use of active learning brings definitive advantages in challenging databases when accompanied with powerful feature representations. The second part of our work deals with active learning on sketches containing more than one object, the so-called \scenes