Abstract
According to the trend in the modern society that utilizes various image related services and products, the amount of images created by diverse personal and industrial devices is immeasurably voluminous. The adoption of very uncommon or unique identifiers or index attributes with admissible storage requirement and adequate data representation enables massive image databases to process demanded operations effectively and efficiently. For the last decades, various content-based image retrieval techniques have been studied and contributed to support indexing in large digital image databases. However, the complexity, high processing cost of the content-based image retrieval techniques might create inefficiency regarding the configuration of a high-performance image database even though satisfying their own objectives. Moreover the indexing methods with the property of low cardinality might need additional indexing in order to provide strong uniqueness. In this paper, we present identifier generation methods for indexing which are efficient and effective in the perspective of cost and indexing performance as well. The proposed methods exploit the distribution of line segments and luminance areas in an image in order to compose identifiers with high cardinality. From the experimental evaluation, we’ve learned that our approaches are effective and efficient regarding processing time, storage requirement and indexing performance.
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References
Antani S, Kastur R, Jain R (2002) A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recogn 35(4):945–965
Bach JR, Fuller C, Gupta A, Hampapur A, Horowitz B, Humphrey R, Jain RC, Shu C (1996) Virage image search engine: an open framework for image management. In: Proceeding of SPIE, Storage and Retrieval for Image and Video Database IV, San Jose, CA, pp 76-87
Bradski G, Kaehler A (2008) Learning openCV. O’Reily Media
Bransnett P, Bober MZ (2007) Robust visual identifier using the trace transforms. In: Proceedings of Visual Information Engineering Conference (VIE), pp 25-27
Bresenham JE (1965) Algorithm for computer control of a digital plotter. IBM Syst J 4(1):25–30
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell PAMI-8(6):679–698
Chatzichristofis SA, Boutalis YS (2008) CEDD - color and edge directivity descriptor - a compact descriptor for image indexing and retrieval. In: Proceedings of the 6th International Conference, ICVS 2008, pp 312-322
Danish M, Rawat R, Sharma R (2013) A survey: content based image retrieval based on color, texture, shape & neuro fuzzy. Int J Eng Res Appl 3(5):839–844
Doller M, Tous R, Temmermans F, Yoon K, Park J, Kim Y, Stegmaier F, Delgado J (2013) JPEG’s JPSearch standard: harmonizing image management and search. IEEE Multimedia 20(4):38–48
Gonzalez RC (2007) Digital image processing. Prentice Hall, 3rd edn
Idris F, Panchanathan S (1997) Review of image and video indexing techniques. J Vis Commun Image Represent 8(2):146–166
ISO/IEC 15938-3:2002/AMD 3:2010 (2010) Information technology - multimedia content description interface - part 3: visual, amendment 3: image signature tools
ISO/IEC 15938-8:2002/AMD 5:2012 (2012) Information technology - multimedia content description interface - part 8: extraction and use of MPEG-7 description, amendment 5: extraction and matching of image signature tools,
Kaushal M, Singh A, Singh B (2010) Adaptive thresholding for edge detection in gray scale images. Int J Eng Sci Technol 2(6):2077–2208
Kiryati N, Eldar Y, Bruckstein AM (1991) A probabilistic Hough transform. Pattern Recogn 24(3):303–316
Kobayashi T, Higuchi T, Miyajima T, Otsu N (2009) Recognition of dynamic texture patterns using CHLAC features. In: Proceedings of Bio-inspired Learning and Intelligent Systems for Security, BLISS ‘09. Edinburgh, pp 58-60
Lananiere R (2011) OpenCV 2 computer vision application programming cookbook. Packt Publishing
Liu Y, Zhang D, Lu G, Ma W (2007) A survey on the use of content-based image retrieval with high-level semantics. Pattern Recogn 40(1):262–282
Nixon M, Aguado AS (2012) Feature extraction & image processing for computer vision. Academic Press, 3rd edn
Obeid M, Jedynak B, Daoudi M (2001) Image indexing & retrieval using intermediate features. In: Proceedings of the ninth ACM international conference of Multimedia. pp 531-533
Pabboju S, Reddy AVG (2009) A novel approach for content-based image indexing and retrieval system using global and region features. Int J Comput Sci Netw Secur 9(2):119–130
Paschalakis S, Iwamoto K, Bransnett P, Sprljan N, Oa;mi R, Nomura T, Yamada A, Bober M (2012) The MPEG-7 video signature tools for content identification. Circuits and systems for video technology. IEEE Trans 22(7):1050–1063
Powell G (2005) Beginning Database Design. Wrox
Prewitt JMS (1970) Object enhancement and extraction, In: Picture programming and psychopictorics. Academic Press
Raoui Y, Bouyakhf EH, Devy M, Regragui F (2011) Global and local image descriptors for content based image retrieval and object recognition. Appl Math Sci 5(42):2109–2136
Salomon D (1991) Computer graphics and geometric modeling. Springer, New York
Schettini R, Ciocca G, Zuffi S (2002) A Survey of Methods for colour Image Indexing and Retrieval in Image Databases. Colour Image Science: Exploiting Digital Media, Wiley
Vega J, Murari A, Pereira A, Portas A, Castro P (2008) Intelligent technique to search for patterns within images in massive databases. Rev Sci Instrum 79(10)
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The present research was conducted by the research fund of Dankook University in 1012.
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Park, JH. Low-cost image indexing for massive database. Multimed Tools Appl 74, 2237–2255 (2015). https://doi.org/10.1007/s11042-014-2026-y
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DOI: https://doi.org/10.1007/s11042-014-2026-y