{"_buckets": {"deposit": "6dcf1f41-ddce-4b5b-bfc4-62daea75d12c"}, "_deposit": {"id": "18173", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "18173"}, "status": "published"}, "_oai": {"id": "oai:soar-ir.repo.nii.ac.jp:00018173", "sets": ["1222"]}, "author_link": ["50693", "50694", "50695"], "item_1628147817048": {"attribute_name": "出版タイプ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_6_alternative_title_1": {"attribute_name": "その他（別言語等）のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "Object Recognition in Images based on Similarity of Superpixels"}]}, "item_6_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2015-10-15", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "8", "bibliographicPageEnd": "1671", "bibliographicPageStart": "1667", "bibliographicVolumeNumber": "56", "bibliographic_titles": [{"bibliographic_title": "情報処理学会論文誌"}]}]}, "item_6_description_20": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "本研究ではエネルギー最小化に基づく画像内の物体認識・領域抽出手法において，類似スーパーピクセルを利用した項を導入することにより性能向上を図る．このために，まず，認識対象のスーパーピクセルと類似するスーパーピクセルをラベル付された学習用画像から取得することで，スーパーピクセル内のクラスを限定する手法を検討する．さらに色差が大きく異なる隣接スーパーピクセルが存在しうるクラスに関する認識率を向上させるための類似スーパーピクセルペアによるペナルティ項の導入手法について提案し，MSRC-21を用いた実験により，認識精度向上を示す． / 本研究ではエネルギー最小化に基づく画像内の物体認識・領域抽出手法において，類似スーパーピクセルを利用した項を導入することにより性能向上を図る．このために，まず，認識対象のスーパーピクセルと類似するスーパーピクセルをラベル付された学習用画像から取得することで，スーパーピクセル内のクラスを限定する手法を検討する．さらに色差が大きく異なる隣接スーパーピクセルが存在しうるクラスに関する認識率を向上させるための類似スーパーピクセルペアによるペナルティ項の導入手法について提案し，MSRC-21を用いた実験により，認識精度向上を示す．/ We propose methods to improve energy-based image segmentation by introducing term which reflect similarity of superpixels. We consider restricting possible set of class labels for each superpixel depending on the similarity of superpixels. With ordinary methods, if two adjacent superpixels have different colors, they tend to be classified into different classes. It is not always true depending on the class they belong. In this paper we modify ordinary smoothness potential based on the distribution of color-pairs via similarity search of superpixels. We examine improvement of recognition accuracy using MSRC-21 dataset. 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This material is published on this web site with the agreement of the author (s) and the IPSJ. Please be complied with Copyright Law of Japan and the Code of Ethics of the IPSJ if any users wish to reproduce, make derivative work, distribute or make available to the public any part or whole thereof. 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This material is published on this web site with the agreement of the author (s) and the IPSJ. Please be complied with Copyright Law of Japan and the Code of Ethics of the IPSJ if any users wish to reproduce, make derivative work, distribute or make available to the public any part or whole thereof. 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