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Click Carving: Interactive Object Segmentation in Images and Videos with Point Clicks

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Abstract

We present a novel form of interactive object segmentation called Click Carving which enables accurate segmentation of objects in images and videos with only a few point clicks. Whereas conventional interactive pipelines take the user’s initialization as a starting point, we show the value in the system taking lead even in initialization. In particular, for a given image or a video frame, the system precomputes a ranked list of thousands of possible segmentation hypotheses (also referred to as object region proposals) using appearance and motion cues. Then, the user looks at the top ranked proposals, and clicks on the object boundary to carve away erroneous ones. This process iterates (typically 2–3 times), and each time the system revises the top ranked proposal set, until the user is satisfied with a resulting segmentation mask. In the case of images, this mask is considered as the final object segmentation. However in the case of videos, the object region proposals rely on motion as well, and the resulting segmentation mask in the first frame is further propagated across the video to obtain a complete spatio-temporal object tube. On six challenging image and video datasets, we provide extensive comparisons with both existing work and simpler alternative methods. In all, the proposed Click Carving approach strikes an excellent of accuracy and human effort. It outperforms all similarly fast methods, and is competitive or better than those requiring 2–12 times the effort.

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Notes

  1. More details and videos can be found at: http://vision.cs.utexas.edu/projects/clickcarving/.

  2. Code available at: http://vision.cs.utexas.edu/projects/clickcarving/.

  3. The unsupervised NLC method (Faktor and Irani 2014) reports excellent results on a subset of the Segtrack-v2 dataset; the method achieves state of the art results for that subset. We were unable to reproduce the results using the publicly available NLC code, potentially because of an OS incompatibility.

  4. IVID (Shankar Nagaraja et al. 2015) does not report annotation times for Segtrack-v2. Also, VSB100 dataset wasn’t used in their experiments.

  5. More details and videos can be found at: http://vision.cs.utexas.edu/projects/clickcarving/.

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Acknowledgements

This research is supported in part by ONR PECASE N00014-15-1-2291, NSF IIS-1514118, a gift from Qualcomm and a gift from AWS Machine Learning. We would like to thank Shankar Nagaraja for providing the iVideoseg dataset timing data. We also thank all the participants in our user studies.

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  1. The University of Texas at Austin, Austin, USA

    Suyog Dutt Jain & Kristen Grauman

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  1. Suyog Dutt Jain
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Correspondence to Suyog Dutt Jain.

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Jain, S.D., Grauman, K. Click Carving: Interactive Object Segmentation in Images and Videos with Point Clicks. Int J Comput Vis 127, 1321–1344 (2019). https://doi.org/10.1007/s11263-019-01184-2

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