SIGNIFICANCE

1. Development of Ontologies for Semantic Image Mining: First Phase; An Ontology Based Approach to Classify Galaxy Images Wilmer Arroyo Ph.D. Student in Computer Engineering Institute for Computing and Informatics Studies Advisor: Dr. Fernando Vega INTRODUCTION Initially image research was oriented on how to correctly identify digital images stored in a database or in similar structures from a query request from a user, now the field has extended to the inclusion of the concept of multimedia information retrieval (MIR). The MIR paradigm comprises the search for information relevant to the user in document bases that has multiple digital formats such as audio, video, text and images. Several disciplines are devoted to developing frameworks, languages, techniques and methodologies for image search, retrieval, interpretation and eventually knowledge generation based on the image content. However, since the beginning of the discipline it was clear that at least two distinct but intimately related levels were involved on image searches and analysis: the physical-computational paradigm and the semantically-computational paradigm. The physical-computational level deals with the physical attributes and characteristics of an image, and the methodologies for computational efficiency and accuracy. The semantically-computational relates to the high level meaning contained in the images, and the computational representation and analysis of that meaning. To build the semantic-computational level it has been necessary to include the development and generation of ontologies for image search and understanding. • METHODOLOGY Experimental Design • The proposed expert system will use the current Hubble classification scheme as an ontology and generate “archetypal” galaxies that match the Hubble scheme. The system then generates an ontology of galactic shapes and uses it as the image classification mechanism. • Tasks • Develop the ontology of galactic shapes using the Hubble scheme. This may include inclusion in the ontology of rotated and oblique shapes. • Generate the set of features for each of the ontological shapes • Use image classification mechanisms for image analysis • Develop classifiers • Produce and analyze experimental results REFERENCES [BDLT2003] Besson, L.; Da Costa, A.; Leclercq, E.; Terrasse, M.N.; A CBIR-framework: using both syntactical and semantical information for image description, Database Engineering and Applications Symposium, 2003. Proceedings. Seventh International 16-18 July 2003 Page(s):385 - 390 [BKP2002] Breen, C.; Khan, L.; Ponnusammy, A.; Image Classification Using Neural Networks and Ontologies; Proceedings of the 13th International Workshop on Database and Expert Systems Applications (DEXA’02: 2-6 Sept. 2002Page(s): 98- 102 [BP2005] Brown, R.; Pham, B.; Image Mining and Retrieval Using Hierarchical Support Vector Machines, Multimedia Modelling Conference, 2005. MMM 2005. Proceedings of the 11th International 12-14 Jan. 2005 Page(s):446 – 451 [CAB2005] Celebi, M.E.; Aslandogan, Y.A.; Bergstresser, P.R.; Mining biomedical images with density-based clustering, Information Technology: Coding and Computing, 2005. ITCC 2005, International Conference on Volume 1, 4-6 April 2005 Page(s):163 - 168 Vol. 1 [CSBB1997] Chang, S.; Smith, J.; Beigi, M.; Benitez, A.; Visual Information Retrieval from Large Distributed Online Repositories; COMMUNICATIONS OF THE ACM December 1997/Vol. 40, No. 12, [GAP2003] Goderya, S., Andreasen, J. D., & Philip, N. S., Advances in Automated Algorithms For Morphological Classification of Galaxies Based on Shape Features, in ASP Conf. Ser., Astronomical Data Analysis Software and Systems XIII, eds. F. Ochsenbein, M. Allen, & D. Egret (San Francisco: ASP), 2003, Vol. 314 , 617 [GFP2005] Gomez, J., Fuentes, O., Puerari, I., Two-Dimensional Fitting of Brightness Profiles in Galaxy Images with a Hybrid Algorithm, Springer-Verlag, Berlin, LNAI 3682, pp 179-185, 2005 [CF2004] de la Calleja, J. Fuentes, O., Machine Learning and Image Analysis for Morphological Galaxy Classification, Monthly Notices of the Royal Astronomical Society, 2004, Vol 349, 87-93 • SIGNIFICANCE • The interpretation and understanding of an image is dependent on multiple characteristics such as color, shape, objects, proximity rules, hues, light content and perspective. Also the context explicit and implicit in the image is required to have a correct interpretation of the meaning of the image. • When dealing at the semantic level of an ontology there is what is called the symbol grounding problem also known as the semantic gap which is the difference between the image physical data and the semantic data in the form of annotations or association rules for image representation • The basic scheme used for galaxy classification is the Hubble Turning Fork Scheme in which the galaxies are classified according to their shape. This classification scheme was an empirical development based on early photographs available at the time (circa 1930). Today galaxies are also classified according to their luminosity and spectra to determine their evolutionary history in an attempt to produce an uniform theory of galactic evolution similar to the stellar evolution diagram (Hertzprung-Russell diagram). Nevertheless shape classification is the first approach method in galactic astronomy. During the 2000 decade the Sloan Digital Sky Survey will produce more than 50,000,000 images of galaxies. The results of this survey will impose a huge amount of work in human hours since classification of galaxy images is usually done by visual inspection of photographic plates. However, this task is not easy, because it requires skill and experience, and is also time consuming: catalogues containing human classification take years to complete