Web-Based Intelligent Photograph Management System Enhancing Browsing Experience

Yuki Orii; Takayuki Nozawa; Toshiyuki Kondo

doi:10.20965/jaciii.2010.p0390

single-jc.php

« previous

JACIII Vol.14 No.4 pp. 390-395

doi: 10.20965/jaciii.2010.p0390

(2010)

Paper:

Views over last 60 days: 433

Web-Based Intelligent Photograph Management System Enhancing Browsing Experience

Yuki Orii, Takayuki Nozawa, and Toshiyuki Kondo

Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588, Japan

Received:

December 7, 2009

Accepted:

March 8, 2010

Published:

May 20, 2010

Keywords:

photograph management system, photograph browsing system, clustering method

Abstract

We developed a web-based intelligent photograph browsing system that enables automatic clustering of unstructured digital photograph collection. We conducted a user study to assess the effectiveness of developed photograph browsing system em APC and APCCT). The user task adopted here was finding some target photographs indicated by the experimenter. The results suggest that the clustering method of the photograph browsing systems should be changed according to whether or not the photographs had been taken by the user.

Cite this article as:

Y. Orii, T. Nozawa, and T. Kondo, “Web-Based Intelligent Photograph Management System Enhancing Browsing Experience,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.4, pp. 390-395, 2010.

Data files:

References

[1] A. Kuchinsky, C. Pering, M. Creech, D. Freeze, and J. Gwizdka, “FotoFile: a consumer multimedia organization and retrieval system,” Proc. of Conf. on Human Factors in Computing Systems, CHI’99, pp. 496-503, 1999.
[2] H. Kang and B. Shneiderman, “Visualization Methods for Personal Collections Browsing and Searching in the PhotoFinder,” Proc. of IEEE Int. Conf. on Multimedia and Expo, ICME 2000, pp. 1539-1542, 2000.
[3] B. Bederson, “PhotoMesa: A zoomable image browser using quantum treemaps and bullemaps,” Proc. of ACM Symposium on User Interface Software and Technology, UIST 2001, pp. 71-80, 2001.
[4] Y. Rubner, C Tomasi, and L Guibas, “A metric for distributions with applications to image databases,” Proc. of IEEE Int. Conf. on Computer Vision, 1999.
[5] K. Rodden,W Basalaj, D Sinclair, and K Wood, “Evaluating a visualizationof image similarity,” Proc. of Conf. on Research and Development in Information Retrieval, SIGIR’99, pp. 275-276, 1999.
[6] iPhoto, http://www.apple.com/ilife/iphoto/ 2008.
[7] J. C. Platt, M. Czerwinski, and B. A. Field, “PhotoTOC: Automatic Clustering for Browsing Personal Photographs,” Technical Report MSR-TR-2002-17, Microsoft Research, 2002.
[8] J. C. Platt, “AutoAlbum: Clustering Digital Photographs using Probabilistic Model Merging,” Proc. of Content-based Access of Image and Video Libraries, 2000.
[9] A. Graham, H. Garcia-Molina, A. Paepcke, and T. Winnograd, “Time as Essence for Photo Browsing Through Personal Digital Libraries,” Proc. Joint Conf. on Digital Libraries, 2002.
[10] Exchangeable image file format for digital still cameras: “Exif Version 2.2 (JEITA CP-3451),” Standard of Japan Electronics and Information Technology Industries Association, 2002.
[11] R. O. Duda, R. E. Hart, and D. G. Stork, “Pattern Classification,” John Wiley & Sons, Inc., 2001.
[12] M. Stricker and M. Orengo, “Similarity of Color Images,” Storage and Retrieval for Image and Video Databases, SPIE, pp. 381-392, 1995.
[13] Photobucket.com, http://photobucket.com/ 2009.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] A. Kuchinsky, C. Pering, M. Creech, D. Freeze, and J. Gwizdka, “FotoFile: a consumer multimedia organization and retrieval system,” Proc. of Conf. on Human Factors in Computing Systems, CHI’99, pp. 496-503, 1999.

[2] [2] H. Kang and B. Shneiderman, “Visualization Methods for Personal Collections Browsing and Searching in the PhotoFinder,” Proc. of IEEE Int. Conf. on Multimedia and Expo, ICME 2000, pp. 1539-1542, 2000.

[3] [3] B. Bederson, “PhotoMesa: A zoomable image browser using quantum treemaps and bullemaps,” Proc. of ACM Symposium on User Interface Software and Technology, UIST 2001, pp. 71-80, 2001.

[4] [4] Y. Rubner, C Tomasi, and L Guibas, “A metric for distributions with applications to image databases,” Proc. of IEEE Int. Conf. on Computer Vision, 1999.

[5] [5] K. Rodden,W Basalaj, D Sinclair, and K Wood, “Evaluating a visualizationof image similarity,” Proc. of Conf. on Research and Development in Information Retrieval, SIGIR’99, pp. 275-276, 1999.

[6] [6] iPhoto, http://www.apple.com/ilife/iphoto/ 2008.

[7] [7] J. C. Platt, M. Czerwinski, and B. A. Field, “PhotoTOC: Automatic Clustering for Browsing Personal Photographs,” Technical Report MSR-TR-2002-17, Microsoft Research, 2002.

[8] [8] J. C. Platt, “AutoAlbum: Clustering Digital Photographs using Probabilistic Model Merging,” Proc. of Content-based Access of Image and Video Libraries, 2000.

[9] [9] A. Graham, H. Garcia-Molina, A. Paepcke, and T. Winnograd, “Time as Essence for Photo Browsing Through Personal Digital Libraries,” Proc. Joint Conf. on Digital Libraries, 2002.

[10] [10] Exchangeable image file format for digital still cameras: “Exif Version 2.2 (JEITA CP-3451),” Standard of Japan Electronics and Information Technology Industries Association, 2002.

[11] [11] R. O. Duda, R. E. Hart, and D. G. Stork, “Pattern Classification,” John Wiley & Sons, Inc., 2001.

[12] [12] M. Stricker and M. Orengo, “Similarity of Color Images,” Storage and Retrieval for Image and Video Databases, SPIE, pp. 381-392, 1995.

[13] [13] Photobucket.com, http://photobucket.com/ 2009.