Memory Reduction for Real-Time Document Image Retrieval with a 20 Million Pages Database

言語	英語
種類	国際会議等の予稿
著者	Kazutaka Takeda，Koichi Kise，Masakazu Iwamura
論文名	Memory Reduction for Real-Time Document Image Retrieval with a 20 Million Pages Database
論文誌名	Proc. Fourth International Workshop on Camera-Based Document Analysis and Recognition (CBDAR2011)
ページ	pp.59-64
発表場所	Beijing, China
査読の有無	有
発表の種類	口頭発表
年月	2011年9月
分野	文書画像解析, 文書画像検索
キー	`Takeda2011_CBDAR` (id=1244)

要約

We have introduced the three improvements of Locally Likely Arrangement Hashing (LLAH) in ICDAR2011 to reduce a required amount of memory and increase discrimination power of features. In this paper, we show the experimental results which is obtained on a larger-scale database than that utilized for ICDAR2011. From experimental results, we have confirmed that the proposed method realizes 60% memory reduction and achieves 99.2% accuracy with 49ms/query processing time for the retrieval of a database of 20 million pages.

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出力結果

@inproceedings{Takeda2011_CBDAR, author = {Takeda, Kazutaka and Kise, Koichi and Iwamura, Masakazu}, title = {Memory Reduction for Real-Time Document Image Retrieval with a 20 Million Pages Database}, booktitle = {Proc. Fourth International Workshop on Camera-Based Document Analysis and Recognition (CBDAR2011)}, year = 2011, month = sep, pages = {59--64}, address = {Beijing, China} }

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Memory Reduction for Real-Time Document Image Retrieval with a 20 Million Pages Database

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