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<p>We develop a novel computational model to mimic photographers’ observation techniques for scene decomposition. Central to our model is a hierarchical structure designed to capture human gaze dynamics accurately using the Binarized Normed Gradients (BING) objectness metric for identifying meaningful scene patches. We introduce a strategy called Locality-preserved and Observer-like Active Learning (LOAL) that constructs gaze shift paths (GSP) incrementally, allowing user interaction in the feature selection process. The GSPs are processed through a multi-layer aggregating algorithm, producing deep feature representations encoded into a Gaussian mixture model (GMM), which underpins our image retargeting approach. Our empirical analyses, supported by a user study, show that our method outperforms comparable techniques significantly, achieving a precision rate 3.2% higher than the second-best performer while halving the testing time. This streamlined approach blends aesthetics with algorithmic efficiency, enhancing AI-driven scene analysis.</p>

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A novel feature fusion model to mimic photographers’ active observation for scenery recomposition toward physical education

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月26日

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  • Dongyang Tang,
  • Shang Wang

摘要

We develop a novel computational model to mimic photographers’ observation techniques for scene decomposition. Central to our model is a hierarchical structure designed to capture human gaze dynamics accurately using the Binarized Normed Gradients (BING) objectness metric for identifying meaningful scene patches. We introduce a strategy called Locality-preserved and Observer-like Active Learning (LOAL) that constructs gaze shift paths (GSP) incrementally, allowing user interaction in the feature selection process. The GSPs are processed through a multi-layer aggregating algorithm, producing deep feature representations encoded into a Gaussian mixture model (GMM), which underpins our image retargeting approach. Our empirical analyses, supported by a user study, show that our method outperforms comparable techniques significantly, achieving a precision rate 3.2% higher than the second-best performer while halving the testing time. This streamlined approach blends aesthetics with algorithmic efficiency, enhancing AI-driven scene analysis.