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A Shape Reconstructability Measure of Object Part Importance with Applications to Object Detection and Localization

机译:目标零件重要性的形状可重构性度量及其在目标检测和定位中的应用

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We propose a computational model which computes the importance of 2-D object shape parts, and we apply it to detect and localize objects with and without occlusions. The importance of a shape part (a localized contour fragment) is considered from the perspective of its contribution to the perception and recognition of the global shape of the object. Accordingly, the part importance measure is defined based on the ability to estimate/recall the global shapes of objects from the local part, namely the part's "shape reconstructability". More precisely, the shape reconstructability of a part is determined by two factors-part variation and part uniqueness. (i) Part variation measures the precision of the global shape reconstruction, i.e. the consistency of the reconstructed global shape with the true object shape; and (ii) part uniqueness quantifies the ambiguity of matching the part to the object, i.e. taking into account that the part could be matched to the object at several different locations. Taking both these factors into consideration, an information theoretic formulation is proposed to measure part importance by the conditional entropy of the reconstruction of the object shape from the part. Experimental results demonstrate the benefit with the proposed part importance in object detection, including the improvement of detection rate, localization accuracy, and detection efficiency. By comparing with other state-of-theart object detectors in a challenging but common scenario, object detection with occlusions, we show a considerable improvement using the proposed importance measure, with the detection rate increased over 10 %. On a subset of the challenging PASCAL dataset, the Interpolated Average Precision (as used in the PASCAL VOC challenge) is improved by 4-8 %. Moreover, we perform a psychological experiment which provides evidence suggesting that humans use a similar measure for part importance when perceiving and recognizing shapes.
机译:我们提出了一种计算模型,该模型可计算二维对象形状部分的重要性,并将其应用于检测和定位有无遮挡的对象。从形状部分(局部轮廓片段)对感知和识别对象整体形状的贡献的角度考虑了其重要性。因此,基于从局部估计/调用对象的整体形状的能力,即零件的“形状可重构性”,来定义零件重要性度量。更准确地说,零件的形状可重构性由零件变化和零件唯一性两个因素决定。 (i)零件变化量度了整体形状重建的精度,即重建的整体形状与真实物体形状的一致性; (ii)零件唯一性量化了将零件与物体匹配的不确定性,即考虑到零件可以在几个不同的位置与物体匹配。考虑到这两个因素,提出了一种信息理论公式,通过从零件重构对象形状的条件熵来测量零件的重要性。实验结果证明了所提出的部件在物体检测中的重要性,其中包括提高了检测率,定位精度和检测效率。通过在具有挑战性但又很常见的场景中与其他最新的物体检测器(具有遮挡物)进行比较,我们显示出使用所提出的重要性度量的显着改进,检测率提高了10%以上。在具有挑战性的PASCAL数据集的子集上,插值平均精度(用于PASCAL VOC挑战中)提高了4-8%。此外,我们进行了一项心理实验,该实验提供了证据,表明人们在感知和识别形状时会使用类似的措施来衡量零件的重要性。

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