Improvement of Image Retrieval via Site Operator: Findings at a Website Level

Keshavarz, Hamid and Rahimi, Saleh Improvement of Image Retrieval via Site Operator: Findings at a Website Level. International Journal of Information Science and Management, 2018, vol. 16, n. 2, pp. 43-59. [Journal article (Paginated)]

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English abstract

The current paper aimed to explore how image indexing and annotating could improve image retrieval via site operator command. Also was among the goals of the study to compare the effectiveness of different codes assigned to sample images in retrieval ranks of images by Google search engine. Using quasi-experimental method 100 images were selected, each image was uploaded 9 times by concept-based characteristics on site iiproject.ir. Analysis consists of images which retrieved from the site operator command. Number of images retrieved by the site operator command is 151 images of total 900 that are used in the study. The minimum number of retrieved images is related to “image titles” and the maximum to the criteria images which entitled with Q code. Chi-square statistics showed that the number of images retrieved in various codes was different. The best ranking is related to “image title” and the weakest to “image caption in Farsi”. Images average ranking retrieved in 9 groups were different. Findings reflect problems and issues of image indexing and retrieval and put forward some ways to overcome the challenges identified. Some lacks in image retrieval by Google search engine at website level are identified. Different codes and descriptors show different retrieval ranks and results considerable for designers, indexers and even users.

Item type: Journal article (Paginated)
Keywords: Visual Communication, Image Indexing, Image Storage and Retrieval, Concept-based Image Indexing, Site Operator, Google Search Engine
Subjects: H. Information sources, supports, channels.
H. Information sources, supports, channels. > HH. Audio-visual, Multimedia.
H. Information sources, supports, channels. > HP. e-resources.
I. Information treatment for information services
I. Information treatment for information services > IC. Index languages, processes and schemes.
I. Information treatment for information services > IG. Information presentation: hypertext, hypermedia.
I. Information treatment for information services > IH. Image systems.
L. Information technology and library technology
Depositing user: Hamid Keshavarz
Date deposited: 31 Mar 2020 09:00
Last modified: 31 Mar 2020 09:00
URI: http://hdl.handle.net/10760/39765

References

Azzam, I.A.A., Leung, C.H.C. & Horwood, J.F. (2004). Implicit Concept-based Image Indexing and Retrieval, 10th International Multimedia Modelling Conference, Brisbane, Australia, 5-7 January, p. 354.

Bar-Ilan, J., Zhitomirsky-Geffet, M., Miller, Y. & Shoham, S. (2012). Tag-based retrieval of images through different interfaces –a user study. Online Information Review, 36(5), 739-757.

Booth, P.F. (2001). Indexing: The manual of good practice. Munich: K. G. Saur.

Chu, H. (2001).Research in Image Indexing and Retrieval as Reflected in the Literature. Journal of the American Society for Information Science and Technology, 52(12), 1011-1018.

Chua, T.S., Pung, H.K., Lu, G.J. & Jong, H.S. (1994). A Concept-Based Image Retrieval System. Proceedings of the 27th Annual Hawaii. International Conference on System Sciences, Wailea, Hawaii, January 4-7. Fadzli, S.A. & Setchi, R. (2012). Concept-based indexing of annotated images using semantic DNA. Engineering Applications of Artificial Intelligence, 25(8), 1644–1655.

Fauzi, F, & Belkhatir, M. (2013). Multifaceted conceptual image indexing on the World Wide Web. Information Processing & Management, 49(2), 420-440.

Fauzi, M.F.A, & Lewis, P.H. (2008). A multiscale approach to texture-based image retrieval. Pattern Analysis and Applications, 11(2), 141-157.

Ferecatu, M., Boujemaa, N. & Crucianu, M. (2008). Semantic interactive image retrieval combining visual and conceptual content description. Multimedia Systems, 13(5-6), 309-322.

Grauman, K. (2010). Efficiently Searching for Similar Images. Communications of the ACM, 53(6): 84-94.

Greisdorf, H, & O’Connor, B. (2002). Modelling what users see when they look at images: A cognitive viewpoint. Journal of Documentation, 58(1), 6-29.

Jang, T.S. (2002). Image indexing and retrieval using formal concept analysis. MA thesis, Windsor University. Canada.

Jayaratne, L. (2006). Enhancing retrieval of images on the web through effective use of associated text and semantics from low-level image features. PhD Dissertation, School of Computing and Mathematics, University of Western Sydney.

Krause, M.G. (1988). Intellectual problems of indexing picture collections. Audiovisual Librarian, 14(4), 73-81.

Layne, S. S. (1986). Analyzing the subject of a picture. A theoretical approach. Cataloging and classification quarterly, 6(3), 39-52.

Lee, H.J, & Neal, D. (2010). A new model for semantic photograph description combining basic levels and user-assigned descriptors. Journal of Information Science, 36(5), 547-565.

Ménard, E. (2009). Images: indexing for accessibility in a multi-lingual environment–challenges and perspectives. The Indexer, 27(2), 70-76.

Mills, R. (2011). Bluff your way in image management. Multimedia Information & Technology, 37(1), 24-25.

Panofsky, E. (1955). Meaning in the Visual Arts, Anchor, NewYork, NY. Patil, R.C. & Durugkar, S. R (2015). Content Based Image Re-ranking using Indexing Methods, International Journal of Emerging Technology and Advanced Engineering, 5(8), 447-453.

Powell, R.R. (1997). Basic research methods for librarians. 3rd ed. Greenich, CT: Ablex Publishing.

Rorissa, A. (2008). User-generated descriptions of individual images versus labels of groups of images: A comparison using basic level theory. Information Processing and Management. 44 (5), 1741–1753. Setchi, R. Tang, Q. & Stankov, I. (2011). Semantic-based information retrieval in support of concept design. Advanced Engineering Informatics, 25(2), 131-146.

Smits, G., Plu, M. & Bellec, P. (2006). Personal Semantic Indexation of Images Using Textual Annotations. SAMT, LNCS 4306, 71–85.

Vadivel, A., Sural, S. & Majumdar, A. K. (2009). Image retrieval from the web using multiple features. Online Information Review, 33(6), 1169-1188.

Vrochidis, S., Moumtzidou, A. & Kompatsiaris, I. (2012). Concept-based patent image retrieval. World Patent Information, 34(4), 292–303.

Westerveld, T.H.W. (2000). Image Retrieval: Content versus Context. In Proceedings of the Conference on Content-Based Multimedia Information Access, RIAO, Paris, April, 12-14, 276-284.


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