A study on interest point guided visual saliency
A study on interest point guided visual saliency
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DOI：
10.1109/pcs.2015.7170096
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Xianguo Zhang;Shiqi Wang;Siwei Ma;Wen Gao
中科院分区：
文献类型：
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作者：
Xianguo Zhang;Shiqi Wang;Siwei Ma;Wen Gao
视觉注意是人类视觉系统最重要的特征之一，它推断出视觉场景中吸引人的区域。在过去的几十年里，它一直是一个活跃的研究课题，许多提出的视觉注意模型已经在包括计算机视觉和图像处理在内的广泛领域展示了成功的应用。另一方面，兴趣点检测是另一个热点问题，为视觉检索和增强现实等实时应用带来了实际贡献。本文试图探讨兴趣点与视觉注意之间的关系。通过比较不同兴趣点模型在预测视觉注视方面的性能，进行了信息量分析。研究发现，基于斑点的兴趣点模型总体上优于基于角点的兴趣点模型。此外，我们还提出了一种融合各种兴趣点算法的混合策略，实验结果表明，该方法与现有的一些兴趣点算法相比具有较强的竞争力。
Visual attention is one of the most critical characteristics of human visual system (HVS), which infers the attractive regions in a visual scene. It has been an active research topic over the past decades and many proposed models of visual attention have demonstrated successful applications in a wide range of fields including computer vision and image processing. On the other hand, interest point detection is another hot topic that leads practical contributions to the real-time applications such as visual retrieval and augmented reality. In this paper, we try to investigate the relationship between the interest point and the visual attention. An informative analysis is reported by comparing the performance of different interest point models in predicting the visual fixation. It is found that the blob based interest point model generally outperforms the corner based model. Furthermore, we propose a mixture strategy by integrating all the interest point algorithms, and the experimental results indicate that this proposed method is competitive with some state-of-the-art algorithms.