Abstract
Access to electronic books, electronic journals, and web portals, which may contain graphics (drawings or diagrams) and images, is now ubiquitous. However, users may have photographs that contain graphics or images and want to access an electronic database to retrieve this information. Hence, an effective photograph retrieval method is needed. Although many content-based retrieval methods have been developed for images and graphics, few are designed to retrieve graphics and images simultaneously. Moreover, existing graphics retrieval methods use contour-based rather than pixel-based approaches. Contour-based methods, which are concerned with lines or curves, are inappropriate for images. To retrieve graphics and images simultaneously, this work applies an adaptive retrieval method. The proposed method uses histograms of oriented gradient (HOG) as pixel-based features. However, the characteristics of graphics and images differ, and this affects feature extraction and retrieval accuracy. Thus, an adaptive method is proposed that selects different HOG-based features for retrieving graphics and images. Experimental results demonstrate the proposed method has high retrieval accuracy even under noisy conditions.















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References
Abbasi S, Mokhtarian F, Kittler J (1999) Curvature scale space image in shape similarity retrieval. Multimed Syst 7(6):467–476
Alajlan N, Kamel M, Freeman G (2008) Geometry-based image retrieval in binary image databases. IEEE Trans Pattern Anal Mach Intell 30(6):1003–1013
Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):705–522
Canny JF (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–714
Chalechale A, Naghdy G, Mertins A (2005) Sketch-based image matching using angular partitioning. IEEE Trans Syst Man Cybern Syst Hum 35(1):28–41
Chi Y, Leung MKH (2007) ALSBIR: a local-structure-based image retrieval. Pattern Recogn 40(1):244–261
Chio J-W, Chen S-Y (2012) Illustration extraction from video streams. J Pattern Recogn Res 7(1):56–71
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Proc IEEE Conf Comput Vision Pattern Recogn 1:886–893
Datta R, Joshi D, Lia J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2):1–60
Duda RO, Hart PE, Stork DG (2001) Pattern Classification, 2nd, Wiley
Huang Y-W, Liu C-C, Chen S-Y (2007) Graph/image legend retrieval. Asian J Health Inform Sci 2, nos. 1–4:79–102
Kahn CE Jr, Kalpathy-Cramer J, Lam CA, Eldredge CE (2012) Accurate determination of imaging modality using an ensemble of text- and image-based classifiers. J Digital Imag 25(1):37–42
Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans Multimed Comput Commun Appl 2(1):1–19
Ling H, Jacobs D (2007) Shape classification using the inner-distance. IEEE Trans Pattern Anal Mach Intell 29(2):286–299
Liu R, Wang Y, Baba T, Masumoto D (2010) Shape detection from line drawings with local neighborhood structure. Pattern Recogn 43(5):1907–1916
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Müller H, Kalpathy-Cramer J, Eggel I, Bedrick S, Kahn CE Jr, Hersh W, “Overview of the CLEF 2010 medical image retrieval track,” http://clef2010.org/resources/proceedings/ImageCLEF2010_medOverview
Qi H, Li K, Shen Y, Qu W (2010) An effective solution for trademark image retrieval by combining shape description and feature matching. Pattern Recogn 43(6):2017–2027
Sidiropoulos P, Vrochidis S, Kompatsiarisa I (2011) Content-based binary image retrieval using the adaptive hierarchical density histogram. Pattern Recogn 44(4):739–750
Su S-Z, Chen S-Y, Li S-Z, Li S-A, Duh D-J (2010) Structured local binary Haar pattern for pixel-based graphics retrieval. Electron Lett 46(14):996–998
Torralba A, Fergus R, Freeman WT (2008) Eighty million tiny images: a large dataset for non-parametric object and scene recognition. IEEE Trans Pattern Anal Mach Intell 30(11):1958–1970
Vrochidis S, Papadopoulos S, Moumtzidou A, Sidiropoulos P, Pianta E, Kompatsiaris I (2010) Towards content-based patent image retrieval: a framework perspective. World Patent Inform 32(2):94–106
Acknowledgement
The authors would like to thank P. Sidiropoulos, S. Vrochidis, and I. Kompatsiarisa for providing patent image database and the anonymous reviewers for the valuable and insightful comments on the earlier version of this manuscript.
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This work was partially supported by National Science Council of Taiwan, under Grant No. NSC-100-2221-E-155-086 and National Nature Science Foundation of China, under Grant No. 61202143.
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Zhang, HB., Li, SA., Chen, SY. et al. Adaptive photograph retrieval method. Multimed Tools Appl 70, 2189–2209 (2014). https://doi.org/10.1007/s11042-012-1233-7
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DOI: https://doi.org/10.1007/s11042-012-1233-7