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首页> 外文会议>4th International Symposium on Bioelectronics and Bioinformatics >Top down saliency detection via Kullback-Leibler divergence for object recognition
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Top down saliency detection via Kullback-Leibler divergence for object recognition

机译：通过Kullback-Leibler散度进行自上而下的显着性检测以进行对象识别

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摘要

In this paper, we propose a definition of saliency which is useful for object recognition. The desired saliency needs to satisfy the requirements in three levels: 1) representative, it should be a feature of the interest object; 2) discriminative, it needs to be helpful for distinguishing the interest object from the other objects and the background, the usual definitions of saliency for object recognition are able to achieve this goal; 3) easily matched, the saliency of the interest object has to be easy to match correctly. This character, ignored by the usual definitions, is useful for locating the object and estimating the scale and the pose of the object during the recognition. In the proposed definition, the easiness of matching correctly and the discrimination are both measured by the Kullback-Leibler (K-L) divergence. In order to apply the definition to a much broader range of situations, the probability density functions (pdfs) involved in the K-L divergence are not necessary to be restricted to the parametrized families of pdfs, and the K-L divergence is estimated using samples without predicting explicitly the involved pdfs. The experimental results show that the proposed definition of saliency not only is useful for detecting the discriminative features of the interest object, but also improves the accuracy of estimating the support of the object by matching the most salient features.
机译：在本文中，我们提出了显着性定义，该定义对于对象识别很有用。期望的显着性需要满足三个层次的要求：1）具有代表性，它应该是兴趣对象的特征； 2）判别，它有助于将兴趣对象与其他对象和背景区分开，通常用于对象识别的显着性定义可以实现此目标； 3）容易匹配，感兴趣对象的显着性必须易于正确匹配。该字符被常规定义所忽略，可用于在识别过程中定位对象并估算对象的比例和姿势。在提出的定义中，正确匹配的容易程度和区分度均由Kullback-Leibler（K-L）散度度量。为了将定义应用于更大范围的情况，KL散度中涉及的概率密度函数（pdf）不必限于参数化的pdf族，并且使用样本估计KL散度而无需明确预测涉及的PDF文件。实验结果表明，提出的显着性定义不仅可用于检测感兴趣对象的判别特征，而且还可通过匹配最显着特征来提高估计对象支持度的准确性。

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