Exploring Local and Overall Ordinal Information for Robust Feature Description
Exploring Local and Overall Ordinal Information for Robust Feature Description
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DOI：
10.1109/tpami.2015.2513396
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发表时间：
2016-11
影响因子：
23.6
通讯作者：
Zhenhua Wang;Bin Fan;G. Wang;Fuchao Wu
中科院分区：
文献类型：
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作者：
Zhenhua Wang;Bin Fan;G. Wang;Fuchao Wu
本文的目的是建立强大的特征描述符，探索强度顺序信息的补丁。为此，提出了局部强度顺序模式（LIOP）和整体强度顺序模式（OIOP）来有效地编码每个像素不同方面的强度顺序信息。具体来说，LIOP通过使用像素周围所有相邻采样点之间的强度关系来捕获局部有序信息，而OIOP利用这些采样点的粗略量化的整体强度顺序。这两种模式，然后分别聚合到不同的顺序箱，导致两种特征描述符。此外，由于这两种描述符可以编码互补的顺序信息，它们被组合在一起，以获得一个区分和紧凑的混合强度顺序模式描述符。所有这些描述符都是基于强度的相对关系以旋转不变的方式构造的，使得它们对图像旋转和任何单调的强度变化具有固有的不变性。图像匹配和对象识别的实验结果是令人鼓舞的，证明了我们的描述符的优势，在最先进的。
This paper aims to build robust feature descriptors by exploring intensity order information in a patch. To this end, the local intensity order pattern (LIOP) and the overall intensity order pattern (OIOP) are proposed to effectively encode intensity order information of each pixel in different aspects. Specifically, LIOP captures the local ordinal information by using the intensity relationships among all the neighbouring sampling points around a pixel, while OIOP exploits the coarsely quantized overall intensity order of these sampling points. These two kinds of patterns are then separately aggregated into different ordinal bins, leading to two kinds of feature descriptors. Furthermore, as these two kinds of descriptors could encode complementary ordinal information, they are combined together to obtain a discriminative and compact mixed intensity order pattern descriptor. All these descriptors are constructed on the basis of relative relationships of intensities in a rotationally invariant way, making them be inherently invariant to image rotation and any monotonic intensity changes. Experimental results on image matching and object recognition are encouraging, demonstrating the superiorities of our descriptors over the state of the art.