Abstract
This paper presents GraphBPT, a tool for hierarchical representation of images based on binary partition trees. It relies on a new BPT construction algorithm that have interesting tuning properties. Besides, access to image pixels from the tree is achieved efficiently with data compression techniques, and a textual representation of BPT is also provided for interoperability. Finally, we illustrate how the proposed tool takes benefit from probabilistic inference techniques by empowering the BPT with its equivalent factor graph. The relevance of GraphBPT is illustrated in the context of image segmentation.
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References
Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D.: Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing 80, 91–106 (2013)
Blaschke, T., Lang, S., Lorup, E., Strobl, J., Zeil, P.: Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Environmental Information for Planning, Politics and the Public 2, 555–570 (2000)
Walter, V.: Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry and Remote Sensing 58(3–4), 225–238 (2004)
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8), 1915–1929 (2013)
Lefvre, S., Chapel, L., Merciol, F.: Hyperspectral image classification from multiscale description with constrained connectivity and metric learning. In: Proceedings of the 6th International Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 (2014)
Valero, S., Salembier, P., Chanussot, J.: Hyperspectral image representation and processing with binary partition trees. IEEE Transactions on Image Processing 22(4), 1430–1443 (2013)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press (2009)
Salembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE Transactions on Image Processing 7(4), 555–570 (1998)
Jones, R.: Component trees for image filtering and segmentation. In: IEEE Workshop on Nonlinear Signal and Image Processing, NSIP (1997)
Monasse, P., Guichard, F.: Scale-space from a level lines tree. Journal of Visual Communication and Image Representation 11(2), 224–236 (2000)
Garrido, L., Salembier, P., Garcia, D.: Extensive operators in partition lattices for image sequence analysis. Signal Processing 66(2), 157–180 (1998)
Salembier, P., Garrido, L.: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Transactions on Image Processing 9(4), 561–576 (2000)
Salerno, O., Pardàs, M., Vilaplana, V., Marqués, F.: Object recognition based on binary partition trees. In: International Conference on Image Processing, vol. 2, pp. 929–932. IEEE (2004)
Vilaplana, V., Marques, F., Salembier, P.: Binary partition trees for object detection. IEEE Transactions on Image Processing 17(11), 2201–2216 (2008)
Giró-i Nieto, X.: Part-Based Object Retrieval With Binary Partition Trees. PhD thesis, Universitat Politècnica de Catalunya, UPC (2012)
Valero, S., Salembier, P., Chanussot, J.: Hyperspectral image representation and processing with binary partition trees. IEEE Transactions on Image Processing 22(4), 1430–1443 (2013)
Garrido, L.: Hierarchical Region Based Processing of Images and Video Sequences: Application to Filtering, Segmentation and Information Retrieval. PhD thesis, Universitat Politècnica de Catalunya, UPC (2002)
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(2), 353–367 (2011)
Bo, L., Lai, K., Ren, X., Fox, D.: Object recognition with hierarchical kernel descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1729–1736. IEEE (2011)
Nowozin, S., Lampert, C.H.: Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision 6(3–4), 185–365 (2011)
Mooij, J.M.: libDAI: A free and open source C++ library for discrete approximate inference in graphical models. Journal of Machine Learning Research 11, 2169–2173 (2010)
McAuley, J., de Campos, T., Csurka, G., Perronnin, F.: Hierarchical image-region labeling via structured learning. In: Proceedings of the British Machine Vision Conference, pp. 49.1–49.11. BMVA Press (2009)
Cooper, G.F.: The computational complexity of probabilistic inference using bayesian belief networks. Artificial Intelligence 42(2), 393–405 (1990)
Najman, L., Cousty, J., Perret, B.: Playing with kruskal: Algorithms for morphological trees in edge-weighted graphs. In: International Symposium on Mathematical Morphology, pp. 135–146 (2013)
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Al-Dujaili, A., Merciol, F., Lefèvre, S. (2015). GraphBPT: An Efficient Hierarchical Data Structure for Image Representation and Probabilistic Inference. In: Benediktsson, J., Chanussot, J., Najman, L., Talbot, H. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2015. Lecture Notes in Computer Science(), vol 9082. Springer, Cham. https://doi.org/10.1007/978-3-319-18720-4_26
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DOI: https://doi.org/10.1007/978-3-319-18720-4_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18719-8
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