Abstract—In this article, we propose a geoconscious content-based image retrieval system based on a P2P mobile agent framework. This system retrieves similar photographs from an image database of location-dependent photographs (e.g., photos of buildings, landmarks, etc.), which use GPS positions for geo-tagging. The P2P mobile agent framework supports intelligent agents. This agent searches similar content image using a query photograph by traversing the P2P network, instead of the mobile device issuing the query. In this paper, we describe the design of the proposed system and a portion of its implementation. This prototype system produces a new peer and rearranges the placement of image agents among peers for workload balancing. Furthermore, we provide the experimental results of our implementation for managing location-dependent image agents, which are clustered with peers in a distributed Delaunay network.
Based on the success and growth of photo-sharing sites such as Flickr, a large number of photographs are being shared over the Internet. The number of uploaded images is increasing rapidly, and an even greater number of images will be shared in the future owing to the increasingly widespread use of mobile devices that have cameras, such as smartphones. Searching similar photographs from such a huge image database is a typical problem in content-based image retrieval (CBIR).
These photographs can be classified into a number of types, including portraits, landscapes, artistic, and documentary photographs. The target of our research is retrieving location-dependent photographs (e.g., photographs of buildings or landmarks). The location of a photograph is indicated by the GPS position, i.e., the latitude and longitude
where the photographer has taken the picture. This embedding of locational information into a photograph is known as geo-tagging.
We studied a method for retrieving similar images efficiently by utilizing the geolocation of images. It is efficient to limit the geographic range when searching for a location-dependent image. Using a combination of the geographic distance and image distance (using image features) also increases the performance of CBIR. We call this type of process “geoconscious” CBIR [1]. In this paper, the term geoconscious means that the geolocation is the primary criterion used for the retrieval of images.
For image retrieval, calculating various image features and comparing their high-dimensional vectors requires a significantly high calculation cost. While the calculation cost of image pairing is relatively low, comparing a photograph with a vast image dataset to find a similar image requires a large amount of computation. Therefore, as such workload increases, the efficiency of our retrieval method has to increase in-scale with the computation power required.
Such a complex CBIR requires intelligent behavior for each entity used in calculating the similarities. The main concept underlying geoconsciousness is the management of intelligent photo agents based on their locations on the P2P network. As intelligent agents on the P2P network work in a scalable manner, finding similar images using image features and geolocations is a suitable method for a mobile agent-based P2P framework. Structured P2P networks exhibit good usage efficiency for a specific search range. Therefore, we adopted P2P Interactive Agent eXtension (PIAX) [2] as such a framework.
PIAX is a structured P2P framework with an enhanced location-oriented service. It can handle intelligent agents that move across a structured overlay network with multiple peers. Agents can be implemented to calculate various types
The 9th International Workshop on Mobile Peer-to-Peer Computing 2013, San Diego (18 March 2013)The 9th International Workshop on Mobile Peer-to-Peer Computing 2013, San Diego (18 March 2013)
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The 9th International Workshop on Mobile Peer-to-Peer Computing 2013, San Diego (18 March 2013)
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The 9th International Workshop on Mobile Peer-to-Peer Computing 2013, San Diego (18 March 2013)
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The 9th International Workshop on Mobile Peer-to-Peer Computing 2013, San Diego (18 March 2013)
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the image distance is unrelated with the geographic distance. Obviously, the performance of the OIFs overcomes both the geographic and image distances. The best radius in this experiment is 113 m for OIF(1) and 130 m for OIF(2). The slope of the OIFs declines after the best radius.
Figure 10. Radius of range search vs. MRR
VI. CONCLUSION
We proposed a design for a geoconscious CBIR system based on a P2P mobile agent framework. Geoconscious CBIR will enhance the image retrieval accuracy for location-dependent photographs by limiting the geographic range for finding the image and using a combination of geographic and image distances. The P2P mobile agent framework will accelerate the performance and scalability of the retrieval.
We used the PIAX P2P mobile agent framework, as it adapts an overlay network and supports a structured P2P architecture. We proposed the design of different agents for this framework, as well as a protocol for constructing an image database and for CBIR. Furthermore, we showed the results of preliminary experiments using the implementation of an image database construction function and a geoconscious image search function.
We are currently combining the database construction and image feature calculation functions together, and developing an additional image retrieval function.
ACKNOWLEDGMENT
This work was supported by KAKENHI (22300035).
REFERENCES
[1] J. Kamahara, N. Tanaka, K. Hirayama, T. Nagamatsu, Y. Teranishi, and Y. Ariyoshi, “Design of GeoConscious P2P Content-based Image Retrieval,” Proc. 7th Intl. Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA2012), Nov. 2012.
[2] Y. Teranishi, “PIAX: Toward a Framework for Sensor
Overlay Network,” Proc. 6th Annual IEEE Consumer Communications & Networking Conference (CCNC 2009 Workshops), Jan. 2009, pp. 1-5, http://piax.org/en/.
[3] J. Li and G. Zhang, “The State of the Art in Content-Based Image Retrieval in P2P Networks,” Proc. ACM 2nd International Conference on Internet Multimedia Computing and Service (ICIMCS’10), Dec. 2010, pp. 143-146, doi: 978-1-4503-0460-3/10/12.
[4] M. Batko, V. Dohnal, D. Novak, and J. Sedmidubsky, “MUFIN: A Multi-Feature Indexing Network,” Proc. IEEE 2nd International Workshop on Similarity Search and Applications (SISAP’09), Aug. 2009, pp. 158-159, doi: 10.1109/SISAP.2009.24.
[5] X. Meng, C. Feng, and Y. Wang, “Efficient Image Retrieval in P2P Using Distributed TS-SOM and Relevance Feedback,” Proc. International Symposium on Distributed Computing and Applications to Business; Engineering and Science (DCABES2007), Aug. 2007, pp. 1066-1071.
[6] J. Luo, D. Joshi, J. Yu, and A. Gallagher, “Geotagging in Multimedia and Computer Vision—A Survey,” Multimedia and Tools Application, vol. 51, no. 1, Jan. 2011, pp. 187-211, doi:10.1007/s11042-010-0623-y.
[7] Y. Li, D. J. Crandall, and D. P. Huttenlocher, “Landmark Classification in Large-scale Image Collections,” Proc. IEEE 12th International Conference on Computer Vision (ICCV 2009), Sept. 2009, pp. 1957-1964, doi: 10.1109/ICCV.2009.5459432.
[8] X. Li, C. Wu, C. Zach, S. Lazebnik, and J. M. Frahm, “Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs,” Proc. ACM 10th European Conference on Computer Vision (ECCV ’08), Oct. 2008, pp.427-440, doi: 10.1007/978-3-540-88682-2_33.
[9] F. Aurenhammer, “Voronoi Diagrams — A Survey of a Fundamental Geometric Data Structure,” ACM Computing Surveys, vol. 23, no. 3, Sept. 1991, pp. 345-405, doi: 0360-0300/91/0900-0345.
[10] K. Hirayama, T. Matsui, and M. Yokoo, “Adaptive Price Update in Distributed Lagrangian Relaxation Protocol,” Proc. the 8th International Joint Conference on Autonomous Agents & Multi-Agent Systems (AAMAS-2009), May 2009, pp. 1033-1040.
[11] M. Ohnishi, Y. Minamoto, T. Eguchi, H. Kato, R. Nishide, and S. Ueshima, “Autonomous and Distributive Generation Algorithm of Delaunay Network for P2P Model Utilizing Node Location,” IPSJ Transaction on Database, vol. 47, Mar. 2006, pp. 51-64.
[12] J. Kamahara, T. Nagamatsu, and N. Tanaka, “Conjunctive Ranking Function using Geographic Distance and Image Distance for Geotagged Image Retrieval,” Proc. the ACM multimedia 2012 workshop on Geotagging and its Applications in Multimedia (GeoMM’12) , Nov. 2012, pp. 9-14.
[13] D. R. Radev, H. Qi, H. Wu, and W. Fan, “Evaluating Web-based Question Answering Systems.” Proc. of 3rd Int. Conf. on Language Resources and Evaluation (LREC 2002), May. 2002.
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Geographic Distance Image Distance
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The 9th International Workshop on Mobile Peer-to-Peer Computing 2013, San Diego (18 March 2013)
