Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are skeptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.

In this paper the application of uncertainty modeling to convolutional neural networks is evaluated. A novel method for adjusting the network's predictions based on uncertainty information is introduced. This allows the network to be either optimistic or pessimistic in it's prediction scores. The proposed method builds on the idea of applying dropout at test time and sampling a predictive mean and variance from the network's output. Besides the methodological aspects, implementation details allowing for a fast evaluation are presented. In the evaluation on the ILSVRC2014 and VOC2011 datasets it will be shown that modeling uncertainty allows for improving the performance of a given model purely at test time without any further training steps.

The detection and classification of acoustic events in various environments is an important task. Its applications range from multimedia analysis to surveillance of humans or even animal life. Several of these tasks require the capability of online processing. Besides many approaches that tackle the task of acoustic event detection, methods that are based on the well known bag-of-features principle also emerged into the field. Acoustic features are calculated for all frames in a given time window. Then, applying the bag-of-features concept, these features are quantized with respect to a learned codebook and a histogram representation is computed. Bag-of-features approaches are particularly interesting for online processing as they have a low computational cost. In this paper, the bag-of-features principle and various extensions are reviewed, including soft quantization, supervised codebook learning, and temporal modeling. Furthermore, Mel and Gammatone frequency cepstral coefficients that originate from psychoacoustic models are used as the underlying feature set for the bag-of-features. The possibility of fusing the results of multiple channels in order to improve the robustness is shown. Two databases are used for the experiments: The DCASE 2013 office live dataset and the ITC-IRST multichannel dataset.

Labeling images is tedious and a costly work that is required for many applications, for example, tagging, grouping and exploring of image collections. It is also necessary for training visual classifiers that recognize scenes or objects. It is therefore desirable to either reduce the human effort or infer additional knowledge by addressing this task with algorithms that allow for learning image annotations in a semi-supervised manner. In this paper, a semi-supervised annotation learning algorithm is introduced that is based on partitioning the data in a multi-view approach. The method is applied to large, diverse image collections of natural scene images. Experiments are performed on the 15 Scenes and SUN databases. It is shown that for sparsely labeled datasets the proposed annotation learning algorithm is able to infer additional knowledge from the unlabeled samples and therefore improve the performance of visual classifiers in comparison to supervised learning. Furthermore, the proposed algorithm outperforms other related semi-supervised learning approaches.

The classification of acoustic events in indoor environments is an important task for many practical applications in smart environments. In this paper a novel approach for classifying acoustic events that is based on a Bag-of-Features approach is proposed. Mel and gammatone frequency cepstral coefficients that originate from psychoacoustic models are used as input features for the Bag-of representation. Rather than using a prior classification or segmentation step to eliminate silence and background noise, Bag-of-Features representations are learned for a background class. Supervised learning of codebooks and temporal coding are shown to improve the recognition rates. Three different databases are used for the experiments: the CLEAR sound event dataset, the D-CASE event dataset and a new set of smart room recordings.

Handwritten historical documents pose extremely challenging problems for automatic analysis. This is due to the high variability observed in handwritten script, the use of writing styles and script types unknown today, the frequently lacking orthographic standardization, and the degradation of the respective documents. Therefore, it is currently out of question to develop general purpose handwriting recognition systems for historical document collections. It is, however, possible to search relatively homogeneous document collections using word spotting techniques. In this paper we consider the analysis of a challenging collection of postcards from the period of World War I delivered by the German military postal service. More specifically, we consider the automatic grouping of mail pieces by spotting potentially identical addressees. As the annotation of such documents is extremely challenging even for trained experts, a manually developed ground truth annotation will, in general, not be available. Furthermore, a reliable segmentation on word level will hardly be possible. With our segmentation-free query-by-example word spotting method we investigate modifications addressing the better generalization to a multi-writer scenario and its application to degraded documents. Promising results could be achieved in this highly challenging scenario.

Lampung Script is a non-cursive script where a rich set of diacritics is used to modify the syllable denoted by a character symbol. Consequently, the analysis of the relation between characters and diacritic marks associated with them plays an important role in the recognition process. As diacritics can appear in three different relative positions with respect to a character (top, bottom, and right) associating them correctly with a character is a challenging problem. In this paper we propose a novel approach for modeling the relations between characters and diacritics in handwritten Lampung documents. First, a document is segmented into characters and diacritic marks. Then every character defines a normalized coordinate system into which nearby diacritics can be mapped. The relation between a diacritic mark and its associated character can then be described by a statistical model. In a writer independent experimental evaluation we investigate models with different degrees of specialization with respect to their capability of predicting the correct character-to diacritic associations. We achieve significant error rate reductions with respect to a naive association model using a nearest-neighbor criterion.

This paper presents a novel method for combining local image features and spatial information for object classification tasks using the Bag-of-Features principle. The feature descriptor is extended by additional spatial information. Hence, similar feature descriptors do not only describe similar image patches, but similar patches in roughly the same region. Different spatial measures are evaluated on the Caltech 101 dataset showing the improvement by incorporating spatial information into the feature descriptor. Furthermore, the method achieves better classification rates than the comparable Spatial Pyramids with lower a dimensional representation.