Abstract
Efficient and possibly intelligent image retrieval is an important task, often required in many fields of human activity. While traditional database indexing techniques exhibit a remarkable performance in textual information retrieval current research in content-based image retrieval is focused on developing novel techniques that are biologically motivated and efficient. It is well known that humans have a remarkable ability to process visual information and to handle the volume and complexity of such information quite efficiently. In this paper, we present a content-based image retrieval platform that is based on a multi-agent architecture. Each agent is responsible for assessing the similarity of the query image to each candidate image contained in a collection based on a specific primitive feature and a corresponding similarity criterion. The outputs of various agents are integrated using one of several voting schemes supported by the system. The system’s performance has been evaluated using various collections of images, as well as images obtained in specific application domains such as medical imaging. The initial evaluation has yielded very promising results.
Similar content being viewed by others
References
Boucher A, Doisy A, Ronot X, Garbay C (1998) A society of goal-oriented agents for the analysis of living cells. Artif Intell Med 14(1,2):183–200 (September/October)
Boujemaa N, Vertan C (2001) Integrated color texture signature for image retrieval. In: Proc. ICISP 2001, 3–5 May 2001, Agadir, Maroc, vol 1, pp 404–411
Chang EY, Li B, Li C (2000) Toward perception-based image retrieval. UCSB technical report, Feb. 2000
Dimitriadis S (2002) An image retrieval platform based on a biologically inspired architecture. MSc thesis, University of Crete
Dimitriadis S, Marias K, Orphanoudakis SC (2003) A versatile image retrieval platfrom based on a multiagent architecture. 6th international conference on visual information systems, Miami
Fan J, Yau DKY, Elmagarmid AK, Aref WG (2001) Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process 10(10) (October)
Halkiadakis G (1999) Agent architecture for a voting system. MSc thesis, University of Crete
Loncaric S (1998) A survey of shape analysis techniques. Pattern Recogn 8(31):983–1001
Ma W-Y, Manjunath BS (1998) A texture thesaurus for browsing large aerial photographs. J Am Soc Inf Sci 49:633–648
Maes P (1994) Modeling adaptive autonomous agents. Artif Life J 1(1 & 2):135–162, MIT
Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8) (Aug.)
Manjunath BS, Wu P, Newsam S, Shin HD (2000) A texture descriptor for browsing and similarity retrieval. Signal Process Image Commun 16:33–48
Muller W, Marchand-Maillet S, Muller H, Pun T (2000) Towards a fair benchmark for image browsers. In: SPIE Photonics East, Voice, Video and Data Communications, Boston, MA, USA, November 5–8 2000
Muller H, Muller W, Marchand-Maillet S, Squire DM, Pun T (2001) Automated benchmarking in content-based image retrieval. International conference on multimedia and exposition, ICME 2001, Tokyo, Japan
Muller H, Muller W, Squire DM, Marchand-Maillet S, Pun T (2001) Performance evaluation in content-based image retrieval: overview and proposals. Pattern Recogn Lett 22:593–601
Palm C, Keysers D, Lehmann T, Spitzer K (2000) Gabor filtering of complex hue/saturation images for color texture classification. In: Wang PP (ed) Procs. 5th JCIS 2, the association for intelligent machinery, Atlantic City, NJ, pp 45–49
Pfeifer R, Scheier C (1999) Understanding intelligence. MIT, Cambridge, MA
Rosch E (1973) Natural categories. Cogn Psychol 4:328–350
Rubner Y, Puzicha J, Tomasi C, Buhmann JM (2001) Empirical evaluation of dissimilarity measures for color and texture. Comput Vis Image Underst
Veltkamp RC, Hagedoorn M (1999) State of the art in shape matching. Technical report UU-CS-1999-27, Utrecht University, The Netherlands
Veltkamp RC, Tanase M, Sent D (2001) Features in content based image retrieval systems: a survey. Kluwer, Norwell, MA
Zabulis X (2002) Perceptually relevant mechanisms for the description and retrieval of visual information. PhD thesis, University of Crete
Zeki S (1994) A vision of the brain. Blackwell Scientific, Cambridge, MA
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dimitriadis, S., Marias, K. & Orphanoudakis, S.C. A multi-agent platform for content-based image retrieval. Multimed Tools Appl 33, 57–72 (2007). https://doi.org/10.1007/s11042-006-0095-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-006-0095-2