用时间序列转换加速图像中可变尺寸区域的搜索，提升效率
Accelerated Sub-Image Search For Variable-Size Patches Identification Based On Virtual Time Series Transformation And Segmentation
- 将图像转为多通道时间序列并分段，大幅压缩二维搜索空间
- 相比穷举法，求解时间最多减少两个数量级，效果相当
- 无需神经网络或预处理，适合农业遥感等实时场景
本文解决两类任务：(i) 在航拍图像中识别与给定参考图像匹配的固定尺寸目标（如草捆）；(ii) 在图像中识别与小尺度参考图像相似的可变尺寸区域（如需定点喷洒的田间地块）。第二类任务中，相似子图经聚类后，通过求解旅行商问题确定边界。两任务均复杂，因相似子图数量事先未知。本文提出一种基于图像沿RGB通道转化为多变量时间序列并进行分割的加速机制，以降低二维搜索空间。在多种合成与真实图像上，对比两种加速机制与穷举法。定量结果显示，该方法求解时间最多减少两个数量级，定性结果视觉表现相当。方法不依赖神经网络，无需任何图像预处理。
原文摘要 · Abstract (English)
This paper addresses two tasks: (i) fixed-size objects such as hay bales are to be identified in an aerial image for a given reference image of the object, and (ii) variable-size patches such as areas on fields requiring spot spraying or other handling are to be identified in an image for a given small-scale reference image. Both tasks are related. The second differs in that identified sub-images similar to the reference image are further clustered before patches contours are determined by solving a traveling salesman problem. Both tasks are complex in that the exact number of similar sub-images is not known a priori. The main discussion of this paper is presentation of an acceleration mechanism for sub-image search that is based on a transformation of an image to multivariate time series along the RGB-channels and subsequent segmentation to reduce the 2D search space in the image. Two variations of the acceleration mechanism are compared to exhaustive search on diverse synthetic and real-world images. Quantitatively, proposed method results in solve time reductions of up to 2 orders of magnitude, while qualitatively delivering comparative visual results. Proposed method is neural network-free and does not use any image pre-processing.
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