Abstract
In recent years, some researchers have put forth the compactness hypothesis, which suggests that similar colours tend to accumulate in the salient region as opposed to the non-salient region in image saliency detection. We discovered that the k-nearest neighbour (kNN) mechanism assumes the presence of similar objects in close proximity. As the kNN method is a supervised learning approach, we introduced an unfixed k value and combined it with the clustering idea of k-means to develop a novel algorithm called kNN clustering. We proposed an object-biased prior and an improved boundary and background prior based on a given compact matrix. Our algorithm was extensively tested on five publicly available datasets. The experimental results demonstrated that it outperformed eight existing top unsupervised models in producing high-quality saliency maps at full resolution. The improved prior methods were particularly effective when employed with existing algorithms lacking prior knowledge, especially with low-performing models.
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The datasets generated during and/or analysed during the current study are available in the [Kaggle] repository, [https://www.kaggle.com/datasets].
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ZX researched the algorithm and wrote the main manuscript text. SZ and ZR was involved in planning and supervising the work. TY and ZJ revised the manuscript.
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Zhou, X., Ren, Z., Zhou, S. et al. Unsupervised Saliency Detection via kNN Mechanism and Object-Biased Prior. Neural Process Lett 55, 8385–8399 (2023). https://doi.org/10.1007/s11063-023-11316-y
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DOI: https://doi.org/10.1007/s11063-023-11316-y