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Beschreibung:
When creating a visual recognition system for a novel task, one of the main burdens is the collection and annotation of data. Often several thousand samples need to be manually reviewed and labeled so that the recognition system achieves the desired accuracy. The goal of this thesis is to provide methods that lower the annotation effort for visual scene and object recognition. These methods are applicable to traditional pattern recognition approaches as well as methods from the field of deep learning. The contributions are three-fold and range from feature augmentation, over semi-supervised learning for natural scene classification to zero-shot object recognition. The contribution in the field of feature augmentation deals with handcrafted feature representations. A novel method for incorporating additional information at feature level has been introduced. This information is subsequently integrated in a Bag-of-Features representation. The additional information can, for example, be of spatial or temporal nature, encoding a local feature's position within a sample in its feature descriptor. The information is quantized and appended to the feature vector and thus also integrated in the unsupervised learning step of the Bag-of-Features representation. As a result more specific codebook entries are computed for different regions within the samples. The results in the field of image classification for natural scenes and objects as well as the field of acoustic event detection, show that the proposed approach allows for learning compact feature representations without reducing the accuracy of the subsequent classification. In the field of semi-supervised learning, a novel approach for learning annotations in large image collections of natural scene images has been proposed. The approach is based on the active learning principle and incorporates multiple views on the data. The views, i.e. different feature representations, are clustered independently of each other. A human in the loop is asked to label each data ...