Scalable Annotation of Fine-Grained Categories Without Experts

論文URL:http://dl.acm.org/citation.cfm?doid=3025453.3025930

論文アブストラクト:We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts. In animals, there is a direct link between taxonomy and visual similarity: e.g. a collie (type of dog) looks more similar to other collies (e.g. smooth collie) than a greyhound (another type of dog). However, in synthetic categories such as cars, objects with similar taxonomy can have very different appearance: e.g. a 2011 Ford F-150 Supercrew-HD looks the same as a 2011 Ford F-150 Supercrew-LL but very different from a 2011 Ford F-150 Supercrew-SVT. We introduce a graph based crowdsourcing algorithm to automatically group visually indistinguishable objects together. Using our workflow, we label 712,430 images by ~1,000 Amazon Mechanical Turk workers; resulting in the largest fine-grained visual dataset reported to date with 2,657 categories of cars annotated at 1/20th the cost of hiring experts.

日本語のまとめ:

機械学習などに必要な教師データのうち、特に細かいカテゴリ分け (Fine-Grained Categories) が必要なものに対して、クラウドソーシングでアノテーションをつけるための戦略を提案して評価した。

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