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In recent years, natural verbal and non-verbal human-robot interaction has attracted an increasing interest. Therefore, models for robustly detecting and describing visual attributes of objects such as, e.g., colors are of great importance. However, in order to learn robust models of visual attributes, large data sets are required. Based on the idea to overcome the shortage of annotated training data...
In this paper, the problem of part detection, description and selection is discussed. This problem is crucial in the learning algorithms of part-based models, but can't be solved well when some candidate parts are extracted from background. This paper studies this problem and introduces a new algorithm, HCRF-PS (Hidden Conditional Random Fields for Part Selection), for part detection, description,...
We present a framework intended to assist users in the task of tagging pictures with content descriptors. Histogram- or correlogram features of manually indicated regions of interest are extracted from a few training images; probabilistic diffusion over these prototypes is used to analyze further images. Since speed is pivotal in interactive applications, we apply a fast algorithm for computing local...
A novel statistical learning approach for automatic annotation of images is presented. A minimum probability of error annotation is feasible with our approach. Firstly, an image is represented as a bag of feature vectors by dividing the image into small blocks, from each of which a six-dimension feature vector is extracted. Secondly, we established the probabilistic formulation for automatic annotation...
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