Abstract
This paper presents a novel approach for image parsing based on nonparametric model in superpixel level. Spatial context and mutual information between object co-occurrence are introduced and applied for improving the accuracy of image parsing. These methods make the probability of object co-occurrence more reliable, and thus the inference of object label from K nearest neighbors is more accurate. Our system integrates the probability of object co-occurrence with the spatial context and mutual information into a Markov Random Field(MRF) framework. Experimental results on SIFTFlow and Barcelona dataset shows that the spatial context and the mutual information are promising methods to improve the accuracy of nonparametric image parsing models.
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Vu, T.L., Choi, SW., Lee, C.H. (2013). Improving Accuracy for Image Parsing Using Spatial Context and Mutual Information. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_23
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DOI: https://doi.org/10.1007/978-3-642-42051-1_23
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