LDAHash: Improved Matching with Smaller Descriptors
LDAHash: Improved Matching with Smaller Descriptors
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DOI：
10.1109/tpami.2011.103
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发表时间：
2012-01-01
影响因子：
23.6
通讯作者：
Fua, Pascal
中科院分区：
文献类型：
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作者：
Strecha, Christoph;Bronstein, Alexander M.;Fua, Pascal
SIFT类局部特征描述符在计算机视觉应用中被广泛采用，例如基于内容的检索，视频分析，复制检测，对象识别，照片旅游和3D重建。特征描述符可以被设计为对于某些类别的光度和几何变换（特别是仿射和强度尺度变换）是不变的。然而，图像可以经历的真实的变换只能以这种方式近似建模，因此大多数描述符在实践中仅近似不变。其次，描述符通常是高维的（例如，SIFT表示为128维向量）。在大规模检索和匹配问题中，这可能会对存储和检索描述符数据带来挑战。我们将描述符向量映射到汉明空间中，其中汉明度量用于比较所得到的表示。这样，我们通过将描述符表示为短二进制字符串来减少描述符的大小，并从示例中学习描述符不变性。我们展示了广泛的实验验证，证明了所提出的方法的优势。
SIFT-like local feature descriptors are ubiquitously employed in computer vision applications such as content-based retrieval, video analysis, copy detection, object recognition, photo tourism, and 3D reconstruction. Feature descriptors can be designed to be invariant to certain classes of photometric and geometric transformations, in particular, affine and intensity scale transformations. However, real transformations that an image can undergo can only be approximately modeled in this way, and thus most descriptors are only approximately invariant in practice. Second, descriptors are usually high dimensional (e.g., SIFT is represented as a 128-dimensional vector). In large-scale retrieval and matching problems, this can pose challenges in storing and retrieving descriptor data. We map the descriptor vectors into the Hamming space in which the Hamming metric is used to compare the resulting representations. This way, we reduce the size of the descriptors by representing them as short binary strings and learn descriptor invariance from examples. We show extensive experimental validation, demonstrating the advantage of the proposed approach.