Image Indexing Using a Color Similarity Metric based on the Human Visual System
The novelty proposed in this study is twofold and consists in the developing of a new color similarity metric based on the human visual system and a new color indexing based on a textual approach. The new color similarity metric proposed is based on the color perception of the human visual system. Consequently the results returned by the indexing system can fulfill as much as possibile the user expectations. We developed a web application to collect the users judgments about the similarities between colors, whose results are used to estimate the metric proposed in this study. In order to index the image's colors, we used a text indexing engine to facilitate the integration of visual features in a database of text documents. The textual signature is build by weighting the image's colors in according to their occurrence in the image. The use of a textual indexing engine, provide us a simple, fast and robust solution to index images. A typical usage of the system proposed in this study, is the development of applications whose data type is both visual and textual. In order to evaluate the proposed method we chose a price comparison engine as a case of study, collecting a series of commercial offers containing the textual description and the image representing a specific commercial offer.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1085097Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2655
 B. Funt, K. Barnard, and L. Martin, "Is machine colour constancy good enough?" in In Proceedings of the 5th European Conference on Computer Vision. Springer, 1998, pp. 445-459.
 B. Berlin and P. Kay, Basic Color Terms: Their Universality and Evolution. Center for the Study of Language and Int 1969.
 A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, "Content-based image retrieval at the end of the early years," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349-1380,2000.
 I. Gallo and A. Nodari, "Learning object detection using multiple neural netwoks," in VISAP 2011. INSTICC Press, 2011.
 M. J. Swain and D. H. Ballard, "Color indexing," International Journal of Computer Vision, vol. 7, pp. 11-32, 1991.
 S. W. Teng and G. Lu, "Image indexing and retrieval based on vector quantization," Pattern Recogn., vol. 40, no. 11, pp. 3299-3316, 2007.
 J. Huang, S. R. Kumar, M. Mitra, W. Zhu, and R. Zabih, "Image indexing using color correlograms," in CVPR '97. Washington, DC, USA: IEEE, 1997, p. 762.
 N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger, "The r*-tree: an efficient and robust access method for points and rectangles," SIGMOD Rec., vol. 19, pp. 322-331, 1990.
 M. Lux and S. A. Chatzichristofis, "Lire: lucene image retrieval: an extensible java cbir library," in MM '08. New York, NY, USA: ACM, 2008, pp. 1085-1088.
 G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (Wiley Series in Pure and Applied Optics). Wiley-Interscience, 2000.
 E. W. Dijkstra, "A note on two problems in connexion with graphs," Numerische Mathematik, vol. 1, no. 1, pp. 269-271, Dec. 1959.
 M. Riedmiller and H. Braun, "A direct adaptive method for faster back-propagation learning: The rprop algorithm," in IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, 1993, pp. 586-591.
 V. Pedoia and e. a. Colli, V., "fMRI analysis software tools: an evaluation framework," in SPIE Medical Imaging 2011, 2011.
 T. Kailath, "The divergence and bhattacharyya distance measures in signal selection," IEEE Transactions on Communications, vol. 15, pp. 52-60, 1967.
 K. Jarvelin and J. Kekalainen, "Cumulated gain-based evaluation of it techniques," ACM Transactions on Information Systems, vol. 20, pp. 422-446, 2002.
 P. Heckbert, "Color image quantization for frame buffer display," Com-puter Graphics, vol. 16, pp. 297-307, 1982.
 M. Gervautz and W. Purgathofer, "Graphics gems," A. S. Glassner, Ed. San Diego, CA, USA: Academic Press Professional, Inc., 1990, ch. A simple method for color quantization: octree quantization, pp. 287-293.