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SalientShape: group saliency in image collections

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Abstract

Efficiently identifying salient objects in large image collections is essential for many applications including image retrieval, surveillance, image annotation, and object recognition. We propose a simple, fast, and effective algorithm for locating and segmenting salient objects by analysing image collections. As a key novelty, we introduce group saliency to achieve superior unsupervised salient object segmentation by extracting salient objects (in collections of pre-filtered images) that maximize between-image similarities and within-image distinctness. To evaluate our method, we construct a large benchmark dataset consisting of 15 K images across multiple categories with 6000+ pixel-accurate ground truth annotations for salient object regions where applicable. In all our tests, group saliency consistently outperforms state-of-the-art single-image saliency algorithms, resulting in both higher precision and better recall. Our algorithm successfully handles image collections, of an order larger than any existing benchmark datasets, consisting of diverse and heterogeneous images from various internet sources.

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Notes

  1. Meta-data is the current industry standard for image retrieval as popularized by search engines like Google image, Flickr, etc.

  2. http://mmcheng.net/gsal/.

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Acknowledgements

We would like to thank the anonymous reviewers for their constructive comments. This research was supported by the 973 Program (2011CB302205), the 863 Program (2009AA01Z327), the Key Project of S&T (2011ZX01042-001-002), and NSFC (U0735001). Ming-Ming Cheng was funded by Google Ph.D. fellowship, IBM Ph.D. fellowship, and New Ph.D. Researcher Award (Ministry of Edu., CN).

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Cheng, MM., Mitra, N.J., Huang, X. et al. SalientShape: group saliency in image collections. Vis Comput 30, 443–453 (2014). https://doi.org/10.1007/s00371-013-0867-4

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