Multi-Scale Improves Boundary Detection in Natural Images

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Multi-Scale Improves Boundary Detection in Natural Images

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by Xiaofeng Ren

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32 - 1 self

BibTeX

@MISC{Ren_multi-scaleimproves,
    author = {Xiaofeng Ren},
    title = {Multi-Scale Improves Boundary Detection in Natural Images},
    year = {}
}

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Abstract

Abstract. In this work we empirically study the multi-scale boundary detection problem in natural images. We utilize local boundary cues including contrast, localization and relative contrast, and train a classifier to integrate them across scales. Our approach successfully combines strengths from both large-scale detection (robust but poor localization) and small-scale detection (detail-preserving but sensitive to clutter). We carry out quantitative evaluations on a variety of boundary and object datasets with human-marked groundtruth. We show that multi-scale boundary detection offers large improvements, ranging from 20% to 50%, over single-scale approaches. This is the first time that multi-scale is demonstrated to improve boundary detection on large datasets of natural images. 1

Keyphrases

natural image multi-scale improves boundary detection small-scale detection local boundary cue large datasets large-scale detection combine strength boundary detection object datasets multi-scale boundary detection problem first time poor localization quantitative evaluation human-marked groundtruth relative contrast single-scale approach

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