Sample Selection, Category Specific Features and Reasoning
نویسندگان
چکیده
In this paper we present our approach to the 2011 ImageClef PhotoAnnotation task, which is based on the well known bag-of-words model. We investigated an approach for selecting the most informative training samples per concept for classification and the impact of fusing the OpponentSIFT feature with the GIST feature which calculates global image statistics, on scene-based concepts. We also incorporated a post-classification processing step, which refined classification results based on rules of inference and exclusion between concepts. The different approaches provided classification gains when compared to the standard bag-of-words model using only the OpponentSIFT feature.
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