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Data Management in Pervasive Systems pp 291–310Cite as

Content-Based Multimedia Retrieval

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Part of the book series: Data-Centric Systems and Applications ((DCSA))

Abstract

Content-based multimedia information retrieval (IR) provides new models and methods for effectively and efficiently “searching” through the huge variety of media that are available in different kinds of repositories (digital libraries, Web portals, social networks, multimedia databases, etc.). In this chapter, we will review the current state of the art of content-based multimedia information retrieval, including the most promising browsing and search paradigms for the several types of multimedia data, and show some cultural heritage applications.

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References

  1. Albanese, M., d’Acierno, A., Moscato, V., Persia, F., Picariello, A.: A multimedia semantic recommender system for cultural heritage applications. In: 2011 Fifth IEEE International Conference on Semantic Computing (ICSC), pp. 403–410. IEEE, Palo Alto (2011)

    Google Scholar 

  2. Amato, F., Mazzeo, A., Penta, A., Picariello, A.: Building rdf ontologies from semi-structured legal documents. In: CISIS, pp. 997–1002 (2008)

    Google Scholar 

  3. Amato, F., Chianese, A., Moscato, V., Picariello, A., Sperli, G.: Snops: a smart environment for cultural heritage applications. In: Proceedings of the Twelfth International Workshop on Web Information and Data Management, pp. 49–56. ACM, Maui (2012)

    Google Scholar 

  4. Amato, F., Mazzeo, A., Moscato, V., Picariello, A.: Building and retrieval of 3d objects in cultural heritage domain. In: 2012 Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), pp. 816–821. IEEE, Palermo (2012)

    Google Scholar 

  5. Amato, F., Chianese, A., Mazzeo, A., Moscato, V., Picariello, A., Piccialli, F.: The talking museum project. Proc. Comput. Sci. 21, 114–121 (2013)

    Article  Google Scholar 

  6. Ardizzone, E., La Cascia, M.: Automatic video database indexing and retrieval. In: Zhang, H., Aigrain, P., Petkovic, D. (eds.) Representation and Retrieval of Video Data in Multimedia Systems, pp. 29–56. Springer, New York (1997). doi:10.1007/978-0-585-31786-13. http://dx.doi.org/10.1007/978-0-585-31786-13

  7. Arman, F., Hsu, A., Chiu, M.Y.: Image processing on encoded video sequences. Multimedia Syst. 1(5), 211–219 (1994). doi:10.1007/BF01268945. http://dx.doi.org/10.1007/BF01268945

  8. Bach, J.R., Fuller, C., Gupta, A., Hampapur, A., Horowitz, B., Humphrey, R., Jain, R.C., Shu, C.F.: Virage image search engine: an open framework for image management. In: Storage and Retrieval for Image and Video Databases (1996). doi:10.1117/12.234785. http://dx.doi.org/10.1117/12.234785

  9. Bahrami, A., Jun, Y.: Methods and systems for context based query formulation and information retrieval. U.S. Patent No. 7,970,786. U.S. Patent and Trademark Office, Washington, DC, June 2011

    Google Scholar 

  10. Barrington, L., Yazdani, M., Turnbull, D., Lanckriet, G.R.: Combining feature kernels for semantic music retrieval. In: ISMIR, pp. 614–619 (2008)

    Google Scholar 

  11. Bolasco, S.: Statistica testuale e text mining: alcuni paradigmi applicativi. Quaderni di Statistica 7, 17–53 (2005)

    Google Scholar 

  12. Bordogna, G., Pasi, G.: A fuzzy linguistic approach generalizing boolean information retrieval: a model and its evaluation. J. Am. Soc. Inf. Sci. 44(2), 70–82 (1993)

    Article  Google Scholar 

  13. Bullock, J.: libxtract: a lightweight library for audio feature extraction. In: Proceedings of the 2007 International Computer Music Conference, vol. 2, pp. 25–28. ICMA, Copenhagen (2007)

    Google Scholar 

  14. Carpineto, C., Romano, G.: A survey of automatic query expansion in information retrieval. ACM Comput. Surv. (CSUR) 44(1), 1 (2012)

    Google Scholar 

  15. Chang, S.K., Liu, S.H.: Picture indexing and abstraction techniques for pictorial databases. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6(4), 475–484 (1984). doi:10.1109/TPAMI.1984.4767552

    Article  Google Scholar 

  16. Chang, S., Chen, W., Meng, H.J., Sundaram, H., Zhong, D.: Videoq: an automated content based video search system using visual cues. In: Proceedings of ACM Multimedia, pp. 313–324 (1997)

    Google Scholar 

  17. Chen, J.Y., Taskiran, C., Delp, E., Bouman, C.: Vibe: a new paradigm for video database browsing and search. In: Proceedings of IEEE Workshop on Content-Based Access of Image and Video Libraries, 1998, pp. 96–100 (1998). doi:10.1109/IVL.1998.694510

    Google Scholar 

  18. Chung, Y.Y., Choi, E.H.C., Liu, L., Shukran, M.A.M., Shi, D.Y., Chen, F.: A new hybrid audio classification algorithm based on svm weight factor and euclidean distance. In: Proceedings of the 2007 Annual Conference on International Conference on Computer Engineering and Applications, CEA’07, pp. 152–157. World Scientific and Engineering Academy and Society, Stevens Point (2007). http://dl.acm.org/citation.cfm?id=1348258.1348286

  19. Clarizia, F., Colace, F., De Santo, M., Greco, L., Napoletano, P.: Mixed graph of terms for query expansion. In: 11th International Conference on Intelligent Systems Design and Applications (ISDA), 2011, pp. 581–586 (2011). doi:10.1109/ISDA.2011.6121718

    Google Scholar 

  20. Colace, F., Santo, M.D., Greco, L.: An adaptive product configurator based on slow intelligence approach. Int. J. Metadata Semant. Ontol. 9(2), 128–137 (2014). doi:10.1504/IJMSO.2014.060340. http://dx.doi.org/10.1504/IJMSO.2014.060340

  21. Colace, F., Santo, M.D., Greco, L., Napoletano, P.: Text classification using a few labeled examples. Comput. Hum. Behav. (2014). doi:10.1016/j.chb.2013.07.043. http://www.sciencedirect.com/science/article/pii/S0747563213002823

  22. Cummins, R.: A standard document score for information retrieval. In: Proceedings of the 2013 Conference on the Theory of Information Retrieval, p. 24. ACM, New York (2013)

    Google Scholar 

  23. D’Agostino, E., Elia, A., Vietri, S.: Lexicon-grammar, electronic dictionaries and local grammars of Italian. In: Leclère, Ch., Laporte, É., Piot, M., Silberztein, M. (eds.) Lexique, Syntaxe et Lexique-Grammaire. Papers in Honour of Maurice Gross, Lingvisticae Investigationes Supplementa, vol. 24. IGML, Amsterdam (2004)

    Google Scholar 

  24. Deliege, F., Chua, B.Y., Pedersen, T.B.: High-level audio features: distributed extraction and similarity search. In: Ninth International Conference on Music Information Retrieval, Philadelphia, pp. 565–570, September 2008

    Google Scholar 

  25. Downie, J.S.: Music information retrieval. Annu. Rev. Inf. Sci. Technol. 37(1), 295–340 (2003). doi:10.1002/aris.1440370108. http://dx.doi.org/10.1002/aris.1440370108

  26. Eakins, J., Graham, M.: Content-based image retrieval, Technical Report (1999)

    Google Scholar 

  27. Edmunds, A., Morris, A.: The problem of information overload in business organisations: a review of the literature. Int. J. Inf. Manag. 20(1), 17–28 (2000)

    Article  Google Scholar 

  28. Egozi, O., Markovitch, S., Gabrilovich, E.: Concept-based information retrieval using explicit semantic analysis. ACM Trans. Inf. Syst. (TOIS) 29(2), 8 (2011)

    Google Scholar 

  29. Elia, A., Vietri, S., Postiglione, A., Monteleone, M., Marano, F.: Data mining modular software system. In: SWWS, pp. 127–133 (2010)

    Google Scholar 

  30. Elia, A., Guglielmo, D., Maisto, A., Pelosi, S.: A linguistic-based method for automatically extracting spatial relations from large non-structured data. In: Algorithms and Architectures for Parallel Processing, pp. 193–200. Springer, Heidelberg (2013)

    Google Scholar 

  31. Fablet, R., Bouthemy, P., Pérez, P.: Statistical motion-based video indexing and retrieval. In: International Conference on Content-Based Multimedia Information Access, pp. 602–619 (2000)

    Google Scholar 

  32. Faloutsos, C., Oard, D.W.: A survey of information retrieval and filtering methods. Technical Report, CS-TR-3514, University of Maryland (1995)

    Google Scholar 

  33. Faloutsos, C., Barber, R., Flickner, M., Hafner, J., Niblack, W., Petkovic, D., Equitz, W.: Efficient and effective querying by image content. J. Intell. Inf. Syst. 3(3-4), 231–262 (1994). doi:10.1007/BF00962238. http://dx.doi.org/10.1007/BF00962238

  34. Fan, J., Elmagarmid, A.K., Zhu, X., Aref, W.G., Wu, L.: Classview: hierarchical video shot classification, indexing, and accessing. Trans. Multimedia 6(1), 70–86 (2004). doi:10.1109/TMM.2003.819583. http://dx.doi.org/10.1109/TMM.2003.819583

  35. Fernández, M., Cantador, I., López, V., Vallet, D., Castells, P., Motta, E.: Semantically enhanced information retrieval: an ontology-based approach. Web Semant. Sci. Serv. Agents World Wide Web 9(4), 434–452 (2011)

    Article  Google Scholar 

  36. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: the qbic system. Computer 28(9), 23–32 (1995). doi:10.1109/2.410146

    Article  Google Scholar 

  37. Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: IJCAI, vol. 7, pp. 1606–1611 (2007)

    Google Scholar 

  38. Gantz, J., Reinsel, D.: Extracting value from chaos. IDC iview, pp. 1–12 (2011)

    Google Scholar 

  39. Grossman, D.A.: Information Retrieval: Algorithms and Heuristics, vol. 15. Springer, New York (2004)

    Book  Google Scholar 

  40. Guo, G., Li, S.Z.: Content-based audio classification and retrieval by support vector machines. IEEE Trans. Neural Netw. 14(1), 209–215 (2003)

    Article  Google Scholar 

  41. Gupta, A., Jain, R.: Visual information retrieval. Commun. ACM 40(5), 70–79 (1997). doi:10.1145/253769.253798. http://doi.acm.org/10.1145/253769.253798

  42. Han, S., Yoon, K., Kweon, I.: A new technique for shot detection and key frames selection in histogram space. In: 12th Workshop on Image Processing and Image Understanding, pp. 475–479 (2000)

    Google Scholar 

  43. Harris, Z.S.: Linguistic transformations for information retrieval. In: Papers in Structural and Transformational Linguistics, pp. 458–471. Springer, Dordrecht (1970)

    Google Scholar 

  44. Hliaoutakis, A., Varelas, G., Voutsakis, E., Petrakis, E.G., Milios, E.: Information retrieval by semantic similarity. Int. J. Semant. Web Inf. Syst. 2(3), 55–73 (2006)

    Article  Google Scholar 

  45. Hopcroft, J.E.: Introduction to Automata Theory, Languages, and Computation. Pearson Education India, New Delhi (1979)

    MATH  Google Scholar 

  46. Hossein, M.: Automatic audio classification using modified two dimensional root cepstral features. In: The International Conference on Electrical Engineering (2008)

    Google Scholar 

  47. Ishikawa, Y., Subramanya, R., Faloutsos, C.: Mindreader: querying databases through multiple examples. Computer Science Department, p. 551 (1998)

    Google Scholar 

  48. Jain, R.: Dynamic vision. In: 9th International Conference on Pattern Recognition, 1988, vol. 1, pp. 226–235 (1988). doi:10.1109/ICPR.1988.28212

    Google Scholar 

  49. Kara, S., Alan, Ö., Sabuncu, O., Akpınar, S., Cicekli, N.K., Alpaslan, F.N.: An ontology-based retrieval system using semantic indexing. Inf. Syst. 37(4), 294–305 (2012)

    Article  Google Scholar 

  50. Klapuri, A., Davy, M. (eds.): Signal Processing Methods for Music Transcription. Springer, New York (2006)

    Google Scholar 

  51. Kurth, F., Gehrmann, T., Müller, M.: The cyclic beat spectrum: tempo-related audio features for time-scale invariant audio identification. In: Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR), Victoria, pp. 35–40, 8–12 October 2006

    Google Scholar 

  52. Landauer, T.K., Dumais, S.T.: A solution to plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol. Rev. 104(2), 211 (1997)

    Article  Google Scholar 

  53. Landauer, R., Swanson, J.: Frequency factors in the thermally activated process. Phys. Rev. 121(6), 1668 (1961)

    Article  Google Scholar 

  54. Lartillot, O., Toiviainen, P.: A matlab toolbox for musical feature extraction from audio. In: International Conference on Digital Audio Effects, pp. 237–244 (2007)

    Google Scholar 

  55. Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl. 2(1), 1–19 (2006). doi:10.1145/1126004.1126005. http://doi.acm.org/10.1145/1126004.1126005

  56. Liu, M., Wan, C., Wang, L.: Content-based audio classification and retrieval using a fuzzy logic system: towards multimedia search engines. Soft Comput. 6(5), 357–364 (2002). doi:10.1007/s00500-002-0189-3. http://dx.doi.org/10.1007/s00500-002-0189-3

  57. Ma, W., Manjunath, B.: Netra: a toolbox for navigating large image databases. In: Proceedings of International Conference on Image Processing, 1997, vol. 1, pp. 568–571 (1997). doi:10.1109/ICIP.1997.647976

    Google Scholar 

  58. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  59. Massoudi, K., Tsagkias, M., de Rijke, M., Weerkamp, W.: Incorporating query expansion and quality indicators in searching microblog posts. In: Advances in Information Retrieval, pp. 362–367. Springer, Heidelberg (2011)

    Google Scholar 

  60. Mckinney, M., Breebaart, J.: Features for audio and music classification. In: Proceedings of the International Symposium on Music Information Retrieval, pp. 151–158 (2003)

    Google Scholar 

  61. Mehrotra, S., Rui, Y., Ortega-Binderberger, M., Huang, T.: Supporting content-based queries over images in mars. In: Proceedings of IEEE International Conference on Multimedia Computing and Systems ’97, pp. 632–633 (1997). doi:10.1109/MMCS.1997.609791

    Google Scholar 

  62. Mitrovic, D., Zeppelzauer, M., Breiteneder, C.: Features for content-based audio retrieval. Adv. Comput. 78, 71–150 (2010). http://dblp.uni-trier.de/db/journals/ac/ac78.htmlMitrovicZB10

  63. Mojsilovic, A., Rogowitz, B.: Capturing image semantics with low-level descriptors. In: Proceedings of 2001 International Conference on Image Processing, vol. 1, pp. 18–21 (2001). doi:10.1109/ICIP.2001.958942

    Google Scholar 

  64. Ogle, V.E., Stonebraker, M.: Chabot: retrieval from a relational database of images. Computer 28(9), 40–48 (1995)

    Article  Google Scholar 

  65. Ohishi, Y., Goto, M., Itou, K., Takeda, K.: A stochastic representation of the dynamics of sung melody. In: Dixon, S., Bainbridge, D., Typke, R. (eds.) ISMIR, pp. 371–372. Austrian Computer Society, Vienna (2007). http://dblp.uni-trier.de/db/conf/ismir/ismir2007.htmlOhishiGIT07

  66. Papathomas, T., Conway, T., Cox, I., Ghosn, J., Miller, M., Minka, T., Yianilos, P.: Psychophysical studies of the performance of an image database retrieval system. In: IS&T/SPIE Symposium on Electronic Imaging: Science and Technology, Conference on Human Vision and Electronic Imaging III, pp. 591–602 (1998)

    Google Scholar 

  67. Patel, A.J.: Systems and methods for highlighting search results. U.S. Patent No. 6,839,702, 4 Jan 2005

    Google Scholar 

  68. Pentland, A., Picard, R., Sclaroff, S.: Photobook: content-based manipulation of image databases. Int. J. Comput. Vis. 18(3), 233–254 (1996). doi:10.1007/BF00123143. http://dx.doi.org/10.1007/BF00123143

  69. Picard, R., Minka, T.: Vision texture for annotation. Multimedia Syst. 3(1), 3–14 (1995). doi:10.1007/BF01236575. http://dx.doi.org/10.1007/BF01236575

  70. Pickard, A.J.: Research Methods in Information. Facet, London (2013)

    Google Scholar 

  71. Pinquier, J., Arias, J., André-Obrecht, R.: Audio classification by search of primary components. In: International Workshop on Image, Video and Audio Retrieval and Mining, Sherbrooke (2004)

    Google Scholar 

  72. Pohle, T., Knees, P., Seyerlehner, K., Widmer, G.: A high-level audio feature for music retrieval and sorting. In: DAFx-10 (2010)

    Google Scholar 

  73. Quinlan, J.R.: Combining instance-based and model-based learning. In: ICML, pp. 236–243 (1993)

    Google Scholar 

  74. Rabiner, L., Juang, B.H.: Fundamentals of Speech Recognition. Prentice-Hall, Upper Saddle River (1993)

    Google Scholar 

  75. Ranchhod, E.: Lexique-grammaire du portugais: prédicats nominaux supportés par estar. Lingvisticae Investigationes 13(2), 351–367 (1989)

    Article  Google Scholar 

  76. Reed, J.: A study on music genre classification based on universal acoustic models. In: ISMIR 2006, pp. 89–94 (2006)

    Google Scholar 

  77. Roa-Valverde, A.J., Sicilia, M.A.: A survey of approaches for ranking on the web of data. Inf. Retr. 17, 295–325 (2014)

    Article  Google Scholar 

  78. Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. J. Am. Soc. Inf. Sci. 27(3), 129–146 (1976)

    Article  Google Scholar 

  79. Rogowitz, B.E., Frese, T., Smith, J., Bouman, C.A., Kalin, E.: Perceptual image similarity experiments. In: Proceedings of the SPIE Human Vision and Electronic Imaging III, vol. 3299 (1998)

    Google Scholar 

  80. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)

    Article  Google Scholar 

  81. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)

    Article  MATH  Google Scholar 

  82. Schnitzer, D.: High-performance music similarity computation and automatic playlist generation. Technical Report, University of Technology, Vienna (2007)

    Google Scholar 

  83. Scholtes, J.C.: Unsupervised learning and the information retrieval problem. In: 1991 IEEE International Joint Conference on Neural Networks, pp. 95–100. IEEE, Seattle (1991)

    Google Scholar 

  84. Sethi, I.K., Coman, I.L., Stan, D.: Mining association rules between low-level image features and high-level concepts (2001). doi:10.1117/12.421083. http://dx.doi.org/10.1117/12.421083

  85. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)

    Article  Google Scholar 

  86. Smith, J.R., Chang, S.F.: Visualseek: a fully automated content-based image query system. In: Proceedings of the Fourth ACM International Conference on Multimedia, MULTIMEDIA ’96, pp. 87–98. ACM, New York (1996). doi: 10.1145/244130.244151. http://doi.acm.org/10.1145/244130.244151

  87. Tamine-Lechani, L., Boughanem, M., Daoud, M.: Evaluation of contextual information retrieval effectiveness: overview of issues and research. Knowl. Inf. Syst. 24(1), 1–34 (2010)

    Article  Google Scholar 

  88. Tzanetakis, G., Ermolinskyi, A., Cook, P.: Pitch histograms in audio and symbolic music information retrieval. In: Proceedings of the Third International Conference on Music Information Retrieval: ISMIR, pp. 31–38 (2002)

    Google Scholar 

  89. Wang, J., Li, J., Wiederhold, G.: Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947–963 (2001). doi:10.1109/34.955109

    Article  Google Scholar 

  90. Wang, A., et al.: An industrial strength audio search algorithm. In: ISMIR, pp. 7–13 (2003)

    Google Scholar 

  91. Wang, X.H., Zhang, D.Q., Gu, T., Pung, H.K.: Ontology based context modeling and reasoning using owl. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, pp. 18–22. IEEE, Orlando (2004)

    Google Scholar 

  92. Wendlandt, E.B., Driscoll, J.R.: Incorporating a semantic analysis into a document retrieval strategy. In: Proceedings of the 14th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 270–279. ACM, New York (1991)

    Google Scholar 

  93. Wold, E., Blum, T., Keislar, D., Wheaten, J.: Content-based classification, search, and retrieval of audio. IEEE MultiMedia 3(3), 27–36 (1996). doi:10.1109/93.556537

    Article  Google Scholar 

  94. Zabih, R., Miller, J., Mai, K.: A feature-based algorithm for detecting and classifying scene breaks. In: Proceedings of the Third ACM International Conference on Multimedia, MULTIMEDIA ’95, pp. 189–200. ACM, New York (1995). doi:10.1145/217279.215266. http://doi.acm.org/10.1145/217279.215266

  95. Zhou, X.S., Huang, T.S.: Cbir: from low-level features to high-level semantics. In: Proceedings of SPIE Image and Video Communication and Processing (2000). doi:10.1117/12.382975. http://dx.doi.org/10.1117/12.382975

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Authors and Affiliations

  1. DIETI, University of Naples Federico II, Naples, Italy

    Flora Amato, Fabio Persia, Silvestro Roberto Poccia & Aniello De Santo

  2. DIEM, University of Salerno, Fisciano, Italy

    Luca Greco

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  1. Flora Amato
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  2. Luca Greco
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  3. Fabio Persia
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  4. Silvestro Roberto Poccia
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  5. Aniello De Santo
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Correspondence to Flora Amato .

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  1. Università di Salerno, Salerno, Italy

    Francesco Colace

  2. Università di Salerno, Salerno, Italy

    Massimo De Santo

  3. Università di Napoli, Napoli, Italy

    Vincenzo Moscato

  4. Università di Napoli, Napoli, Italy

    Antonio Picariello

  5. Politecnico di Milano, Milano, Italy

    Fabio A. Schreiber

  6. Politecnico di Milano, Milano, Italy

    Letizia Tanca

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Amato, F., Greco, L., Persia, F., Poccia, S.R., De Santo, A. (2015). Content-Based Multimedia Retrieval. In: Colace, F., De Santo, M., Moscato, V., Picariello, A., Schreiber, F., Tanca, L. (eds) Data Management in Pervasive Systems. Data-Centric Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20062-0_14

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