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首页> 外文期刊>Mathematical Problems in Engineering >Entropy-Based and Weighted Selective SIFT Clustering as an Energy Aware Framework for Supervised Visual Recognition of Man-Made Structures
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Entropy-Based and Weighted Selective SIFT Clustering as an Energy Aware Framework for Supervised Visual Recognition of Man-Made Structures

机译：基于熵的加权选择性SIFT聚类作为一种能源感知框架，用于监督人造结构的视觉识别

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摘要

Using local invariant features has been proven by published literature to be powerful for image processing and pattern recognition tasks. However, in energy aware environments, these invariant features would not scale easily because of their computational requirements. Motivated to find an efficient building recognition algorithm based on scale invariant feature transform (SIFT) keypoints, we present in this paper uSee, a supervised learning framework which exploits the symmetrical and repetitive structural patterns in buildings to identify subsets of relevant clusters formed by these keypoints. Once an image is captured by a smart phone, uSee preprocesses it using variations in gradient angle- and entropy-based measures before extracting the building signature and comparing its representative SIFT keypoints against a repository of building images. Experimental results on 2 different databases confirm the effectiveness of uSee in delivering, at a greatly reduced computational cost, the high matching scores for building recognition that local descriptors can achieve. With only 14.3% of image SIFT keypoints, uSee exceeded prior literature results by achieving an accuracy of 99.1% on the Zurich Building Database with no manual rotation; thus saving significantly on the computational requirements of the task at hand.
机译：已发表的文献已证明使用局部不变特征对于图像处理和模式识别任务具有强大的功能。然而，在能量敏感的环境中，这些不变特征由于其计算要求而难以缩放。为了找到一种基于尺度不变特征变换（SIFT）关键点的有效建筑物识别算法，我们在本文中介绍了uSee，这是一种监督学习框架，该框架利用建筑物中的对称和重复结构模式来识别由这些关键点形成的相关聚类的子集。一旦智能手机捕获了图像，uSee就会使用基于梯度角度和熵的度量值对其进行预处理，然后提取建筑物特征并将其代表SIFT关键点与建筑物图像库进行比较。在2个不同数据库上的实验结果证实了uSee能够以大大降低的计算成本提供高匹配分数，以建立本地描述符可以实现的高识别分数。仅用14.3％的图像SIFT关键点，uSee即可在苏黎世建筑数据库上实现99.1％的准确度，而无需人工旋转，超出了先前的文献结果；从而大大节省了手头任务的计算需求。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第14期|730143.1-730143.9|共9页
  • 作者

    Ayman El Mobacher; Nicholas Mitri; Mariette Awad;

  • 作者单位

    Electrical and Computer Engineering Department, The American University of Beirut, P.O. Box 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon;

    Electrical and Computer Engineering Department, The American University of Beirut, P.O. Box 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon;

    Electrical and Computer Engineering Department, The American University of Beirut, P.O. Box 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon;

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