Abstract
Local features are used for describing the visual information in a local neighborhood of image pixels. Although using various types of local features can provide complementary information about the pixels, effective integration of these features has remained as a challenging issue. In this paper, we propose a novel segmentation algorithm which aggregates the information obtained from different local features. Starting with an over-segmentation of the input image, local features are fed into a factorization-based framework to construct multiple new representations. We then introduce a novel aggregation model to integrate the new representations. Our proposed model jointly learns the reliability of representations and infers final representation. Final segmentation is obtained by applying post-processing steps on the inferred final representation. Experimental results demonstrate the effectiveness of our algorithm on the Berkeley Segmentation Dataset.
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Zohrizadeh, F., Kheirandishfard, M., Ghasedidizaji, K., Kamangar, F. (2016). Reliability-Based Local Features Aggregation for Image Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_18
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DOI: https://doi.org/10.1007/978-3-319-50835-1_18
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