In this project, we want to consider the field of image classification with the help of a human expert. Image Classification deals with the problem of determining the occurrence of known objects and concepts in an image. We want to extend the classic image classification approach significantly by introducing new paradigms of image representation and active learning with a human expert (i.e. suitable user interaction) in order to make it applicable on real-world image databases.

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The problem of most image classification tasks today lies in the high complexity of calculating the object features as well as in the high number of possible classes and the costly annotation from a human expert. This complexity has a strong influence in the training phase and in the application phase. The goal of this project is to extend the conventional image classification approach by using different levels of quality in the description of an object/concept and different levels of quality in the feedback from the human expert.

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This requires new algorithms that are designed to automatically determine the best level for the object description and the best form of feedback from the human expert. A central aspect is the balance of complexity and gain. The main advantage of the methods that will be developed is the intelligent and adaptive use of resources, which is superior to static methods. The savings in memory and CPU resources will have a great impact for resource-intensive and time- critical tasks (e.g. real-time image classification in a robot). With new forms of feedback from a human expert, the interaction with a classification system will be simplified, which increases the speed and robustness of the training process.

For more information please contact? Nicolas Cebron.

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References:

• Nicolas Cebron.?Active Improvement of Hierarchical Object Features under Budget Constraints,?10th IEEE International Conference on Data Mining (ICDM), Dec. 2010, Sydney, Australia.? DOI: 10.1109/ICDM.2010.74
  Also Technical Report 2011-01, 新万博体育下载_万博体育app【投注官网】 of Augsburg, Institute of Computer Science, Feb. 2011.?[ PDF ]