Apprendimento Automatico di Modelli Visuali con Dati Incompleti

Abstract: The goal of a learning system is to capture patterns and regularities in training data which allow for future classification. Machine learning methods are able to generalize a classification model from labelled training data but difficulties arise when the distribution of the training data is not explicitly modelled. Real world applications offer a massive amount of visual data, but unfortunately labelled data are not always easy to find and the labelling process is costly and time consuming or may not be possible for a lack of knowledge. This work is focused on the learning of discriminative visual models in scenarios with partially annotated or incomplete data. With incomplete data we refer either to the case where only a subset of the training data is labelled or where only a fraction of the training classes is known. We evaluate the problem of learning from incomplete data in three separate computer vision applications, namely people tracking, novel image classification and document image analysis. In video surveillance the input of a tracking system might be interpreted as a set of partially labelled data where there are only few annotated instances of the target and several not annotated samples. Not annotated test data might also deviate from training data because of occlusions, changes in pose or appearance making the target association problem challenging. We exploit a semi supervised learning method to solve the problem of people tracking and we demonstrate with an experimental analysis the effectiveness of the proposed approach. Regarding image categorization, an interesting challenge is represented by the detection of novel categories and subcategories of objects. Assuming that objects can be organized in taxonomies, the instances to be classified may differ from the hierarchy learned from training data and they might share only parent nodes. Our work is devoted to derive a learning model from labelled data able to generalize over data coming from classes not seen during training. Finally, the last part addresses the picture segmentation in document images of old books. Dealing with the layout segmentation of old documents results in a variety of pictorial elements, thus in the difficulty of being able to collect samples representative of this heterogeneity. We propose an effective feature representation and a Support Vector Machines classification along with an experimental evaluation that demonstrate an improvement over baseline methods of document layout analysis even if a detailed model of the input space is not available.

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Coppi, Dalia "Apprendimento Automatico di Modelli Visuali con Dati Incompleti" 2014

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