CNN-aware Binary Map for General Semantic Segmentation

Preprint English OPEN
Ravanbakhsh, Mahdyar ; Mousavi, Hossein ; Nabi, Moin ; Rastegari, Mohammad ; Regazzoni, Carlo (2016)
  • Subject: Computer Science - Computer Vision and Pattern Recognition
    arxiv: Computer Science::Computer Vision and Pattern Recognition | Computer Science::Neural and Evolutionary Computation
    acm: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION

In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary codes are very robust against noise and non-semantic changes in the image. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by large margin.
  • References (27)
    27 references, page 1 of 3

    [5] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR, 2015.

    [6] LC Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” in arXiv:1412.7062, 2014.

    [7] S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. HS Torr, “Conditional random fields as recurrent neural networks,” in ICCV, 2015.

    [8] H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in ICCV, 2015.

    [9] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NIPS, 2012.

    [10] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in arXiv:1409.1556, 2014.

    [11] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in CVPR, 2015.

    [12] J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, “Decaf: A deep convolutional activation feature for generic visual recognition,” in arXiv:1310.1531, 2013.

    [13] J. Shi and J. Malik, “Normalized cuts and image segmentation,” in PAMI. 2000, IEEE.

    [14] Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, “Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval,” in PAMI, 2013.

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