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A Pooling Approach to Modelling Spatial Relations for Image Retrieval and Annotation

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44976

Malinowski,  Mateusz
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44451

Fritz,  Mario
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1411.5190.pdf
(Preprint), 2MB

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Citation

Malinowski, M., & Fritz, M. (2014). A Pooling Approach to Modelling Spatial Relations for Image Retrieval and Annotation. Retrieved from http://arxiv.org/abs/1411.5190.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-4D38-0
Abstract
Over the last two decades we have witnessed strong progress on modeling visual object classes, scenes and attributes that have significantly contributed to automated image understanding. On the other hand, surprisingly little progress has been made on incorporating a spatial representation and reasoning in the inference process. In this work, we propose a pooling interpretation of spatial relations and show how it improves image retrieval and annotations tasks involving spatial language. Due to the complexity of the spatial language, we argue for a learning-based approach that acquires a representation of spatial relations by learning parameters of the pooling operator. We show improvements on previous work on two datasets and two different tasks as well as provide additional insights on a new dataset with an explicit focus on spatial relations.