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  • reading list 2014

    List
    [1]
    Region-based Saliency Detection and Its Application in Object Recognition. Zhixiang Ren, Circuits and Systems for Video Technology, IEEE Transactions on 2013
    [2] 蒋经国传. 陶函.
    [3] 红楼梦.
    [4] Mean shift: a robust approach toward feature space analysis
    [5] Associative Hierarchical CRFs for Object Class Image Segmentation
    [6] Efficient Hierarchical Graph-Based Video Segmentation
    [7] Nonlinear Mean Shift over Riemannian Manifolds
    [8] Learning Hierachical Feature Extractors For Image Recognition. (严乐存学生12年的Ph.D Thesis)
    [9] Learning Feature Hierachies for Object Recognition.
    (严乐存学生12年的Ph.D Thesis)
    [10] Efficient Salient Region Detection with Soft Image Abstraction. ICCV 2013 Ming-Ming Cheng
    [11] Clustering by passing messages between data points. Science 2007 B. Frey http://www.psi.toronto.edu/affinitypropagation/FreyDueckScience07.pdf
    [12] Hierarchical Anity Propagation
    [13] Self-adaptively weighted Co-saliency detection via rank constraint. TIP2014
    [14] Saliency map fusion based on rank-one constraint. ACM MM 2013
    [15] Regularity Preserved Superpixels and Supervovxels. TMM 2014
    [16] 一蓑烟雨任平生 陈如江
    [17] Aggregating local descriptors into a compact image representation. CVPR 2010
    [18]
    Aggregating local descriptors into compact codes. TPAMI 2012
    [19] Boosting Binary Keypoint Descriptors. CVPR 2013
    [20] Discriminative Color Descriptors. CVPR 2013
    [21] Dense Segmentation Aware Descriptors
    [22] Sparse Quantization for Patch Desciption
    [23] Constraints as Features
    [24] Kernel Learning for Extrinsic Classification of Manifold Features
    [25] A max-Margin Riffled Independence Model for Image Tagg Ranking
    [26] Discriminative Re-ranking of Diverse Segmentation
    [27] Weakly Surpervised Dual Clustering for Image Semantic Segmentation
    [28] Constrained Clustering and Its-application to Face Clustering in Videas
    [29] Discriminative Subspace Clustering
    [30] Scalable Sparse Subspace Clustering
    [31] A Principle Deep Random Field Model for Image Segmentation
    [32] Bottom-Up segmentation for Top-Down Detection
    [33] Fast Trust Region for Segmetnation
    [34] Image Segmentation by Cascaded Agglomeration
    [35] Towards Fast and Accurate Segmentation
    [36] Graph Transduction Learning with Connectivity Constraints with Application to Multiple Foreground Cosegmentation
    [37] Deep Learning Shape Priors for Object Segmentation
    [38] A Co-saliency Model of Image Pairs TIP2011
    [39] Cluster-based Co-saliency Detection. Huazhu Fu, TIP 2013.
    [40] Scale-space theory: A basic tool for analysing structures at difference scales. Tony Lindeberg. Journal of Applied Statistics. 1994.
    [50] Feature Detection with Automatic Scale Selection. Tony Lindeberg. 1998. Tech Report.
    [51] 平行宇宙. 加来道雄
    [52] 时间简史
    [53] What is an object? CVPR 2010.
    [54] Efficient Non-Maximum Suppression. ICPR 2006.
    [55] Latent Maximum Margin Clustering. NIPS 2013.
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  • 原文地址:https://www.cnblogs.com/qingliu411/p/3501557.html
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