THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, SNS A Study for Appl
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1 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, SNS A Study for Applications of the Detection Method of Similar Geo-Regions based on Photographic Contents Hiroki TAKIMOTO, Yasutomo KAWANISHI, Ichiro IDE, Takatsugu HIRAYAMA, Keisuke Abstract DOMAN,, Daisuke DEGUCHI,, and Hiroshi MURASE Graduate School of Information Science, Nagoya University Furo-cho, Chikusa-ku, Nagoya-shi, Aichi, Japan School of Engineering, Chukyo University 101 Tokodachi, Kaizu-cho, Toyota-shi, Aichi, , Japan Information Strategy Office, Nagoya University Furo-cho, Chikusa-ku, Nagoya-shi, Aichi, Japan When we visit a place we have never visited, we want to know the atmosphere intuitively. But sometimes it is difficult for us to know the atmosphere of the unfamiliar place. In such a case, we can imagine the atmosphere if we know that the place is similar to another place we already know. Thus we have been studying a method for detecting similar geo-regions based on the contents of social photos. As the applications of the proposed method, we report the result of calculating similarity between geo-regions and groups of photos taken by a person during a trip and also similarity among these groups. Key words Travel, photo, support, recommendation 1. JNTO [1] ,
2 [2] GPS GPS Web 1.com 2 Web Web 3 4 Web Web Web SNS [3]2. [3] [3] SNS SNS 1http:// 2http:// 3http:// 4http://rurubu.travel/2016/9/10 1 SNS SNS SNS [3]2. 1 [3] SNS SNS SNS 1 [3] Mean Shift 2
3 2 DBSCAN 1 74,966 11,676 86,090 77,109 3,646 4,774 4,852 6, ,192 DBSCAN [4] Mean Shift DBSCAN SNS SNS SNS SNS SNS Flickr 5 Flickr API SNS DBSCAN Eps MinPts T Eps = 0.001MinPts = 5T = 0.7 Visual Concept Places205-GoogLeNet [5] 205 Visual Concept 5https:// 6https:// 2,540 3,224,530 35, Google Maps 7 3 Visual Concept 8 Visual Concept 3. [3] Visual Concept https:// 8 3
4 ᆅᅗ 㐺ᙜ ᡤ ࢡ ࢡࡍ 㸪ࢡ ࢡࡋࡓ ᡤ ᆅᇦ 㟷 ࡋ㸪 㢮ఝᆅᇦ ࡍ 㸬 ࢡ ࢡࡍ 㸪㢮ఝᆅᇦ㸦㟷 㸧 ࡘ ሗ ࡍ 㸬 ኸ 㒊㸪 㒊 ୧ᆅᇦෆ ᙳࡉ ࡓ 㸬 ࡀ 㢮ఝᗘ ࡋࡓ 9LVXDO &RQFHSW 㸦ᕥ 㸧 ᑐᛂ㸬 ᆅᇦ㛫 㢮ఝᗘ 㢮ఝᗘ ࡋࡓ 9LVXDO &RQFHSW ᗘ ኸ 㸦 㸧㒊 ᆅᅗ㸸ᕥ ᆅᅗ 㟷㸦 㸧 㢮ఝᗘ ᆅᇦ ᑐᛂࡋ㸪ᆅᇦ ᅖ ᙧ ࡍ 㸬 図 3 可視化ツール 名古屋港水族館を含む地域と横浜の八景島シーパラダイスを含む地域の結 果例 表2 ユーザ 枚数 A ユーザ A I がそれぞれ撮影した写真の枚数 B C D E F G H I 合計 ,253 推薦することもできる 以降では これらについて試験的に検討した結果について 述べる まず 3. 1 節で実験に使用した個人の旅行記録写真の データセットについて述べる 次に 3. 2 節で個人の旅行記録 写真と地域との類似度算出について検討する 最後に 3. 3 節 で個人の旅行記録写真間の類似度算出について検討する 3. 1 個人の旅行記録写真データセット 実験に用いる個人の旅行記録写真のデータセットについて説 明する 20 代から 40 代までの男女計 9 名 以後ユーザ A 図 4 ユーザ A が撮影した写真の一部 I と呼称 が 2015 年 8 月 30 日から 9 月 1 日までの 2 泊 3 日 で京都府 滋賀県 岐阜県に旅行した時に撮影した写真を用い る それぞれが撮影した写真の枚数を表 2 に示す これら合計 9 セットの個人の旅行記録写真をデータセットとして用いる 3. 2 個人の旅行記録写真と地域間の類似度算出 実際に個人の旅行記録写真を用いて これらと類似する地域 を検出した結果を報告する 実験では SNS 投稿写真として で用いたデータセットを用い で述べた条件の下 で 3. 1 節で説明したデータセットと類似する地域を検出した なお 個人の旅行記録写真の特徴記述は地域の特徴記述と同様 で 各写真の Visual Concept を検出し 全ての写真の Visual Concept スコアのヒストグラムの和を計算したものを特徴量と する 個人の旅行記録写真と地域の組み合わせ総数は 22,779 組で そのうち類似地域は 31 組となった 図 5 大宰府を含む地域で撮影された SNS 投稿写真の一部 その中でも 有名な観光地を含む類似地域について取り上げ 考察する ユーザ A の旅行記録写真からは 13 組の類似地域が 建造物の写真や自然の写真を多く撮影していることが分かる 得られ その中の 1 つに有名な観光地である大宰府を含む地域 大宰府を含む地域においては 大宰府天満宮や周辺の自然が多 が存在した 類似度は であった ユーザ A が実際に撮影 く撮影されていることが分かる 図 5 実際に両者の間の類 した写真の一部を図 4 に示す ユーザ A は特に神社 寺など 似度へ貢献した Visual Concept とその貢献度 表 3 を確認す 4
5 3 A Visual Concept Visual Concept 1 temple/east asia botanical garden pavilion pagoda temple/south asia B C D E F G H I A B C D E F G H F Visual Concept F H A B 2 F H 67 Visual Concept 5restaurantcoffee shop Visual Concept Visual Concept rainforestcreek playground Visual Concept Visual Concept 7 H 5 F H Visual Concept Visual Concept 1 restaurant coffee shop playground rainforest creek A B 48 A B A B 4. SNS Mean Shift DBSCAN 5
6 N n=1 S i,j = (V i(n) V i )(V j (n) V j ) N (V n=1 i(n) V i ) 2 (A 1) N (V n=1 j(n) V j ) 2 V l = 1 N V l (n) N n=1 (A 2) 8 B [1] JNTO go.jp/jpn/news/press_releases/pdf/ _1.pdf 2016/5/5. [2] kankocho/page05_ html 2016/5/5. [3] SNS MVE2016-4June [4] M. Ester, H.P. Kriegel, J. Sander, and X. Xu, A densitybased algorithm for discovering clusters in large spatial databases with noise, Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, pp , Aug [5] Web NLC Dec [6] B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, Learning deep features for scene recognition using places database, Advances in Neural Information Processing Systems 27, pp , Dec Visual Concept Visual Concept (A 1) (A 3) f 35 (A 3) C i,j (f)= N (V i (f) V i )(V j (f) V j ) (V n=1 i(n) V i ) 2 (A 3) N (V n=1 j(n) V j ) 2 [3] Visual Concept 1. 2 V i V j Visual Concept N Visual Concept Visual Concept 6
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