A Highly Scalable and Adaptable Co-Learning Framework on Multimodal Data Mining in a Multimedia Database

A Highly Scalable and Adaptable Co-Learning Framework on Multimodal Data Mining in a Multimedia Database

Zhongfei (Mark) Zhang (SUNY Binghamton, USA), Zhen Guo (SUNY Binghamton, USA), Christos Faloutsos (Carnegie Mellon University, USA) and Jia-Yu Pan (Google Inc., USA)
Copyright: © 2013 |Pages: 20
DOI: 10.4018/978-1-4666-2455-9.ch028
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This chapter presents a highly scalable and adaptable co-learning framework on multimodal data mining in a multimedia database. The co-learning framework is based on the multiple instance learning theory. The framework enjoys a strong scalability in the sense that the query time complexity is a constant, independent of the database scale, and the mining effectiveness is also independent of the database scale, allowing facilitating a multimodal querying to a very large scale multimedia database. At the same time, this framework also enjoys a strong adaptability in the sense that it allows incrementally updating the database indexing with a constant operation when the database is dynamically updated with new information. Hence, this framework excels many of the existing multimodal data mining methods in the literature that are neither scalable nor adaptable at all. Theoretic analysis and empirical evaluations are provided to demonstrate the advantage of the strong scalability and adaptability. While this framework is general for multimodal data mining in any specific domains, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance. They have compared the framework with a state-of-the-art multimodal data mining method to demonstrate the effectiveness and the promise of the framework.
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In the machine learning community, MIL has become a focused topic in recent years and has received extensive attention in the literature ever since the classic work of (Dietterich et al, 1997; Auer, 1997; and Maron and Lozano-Perez, 1998). Recent developments on MIL include (Andrews et al, 2003; Andrews and Hofmann; 2004; and Rahmani and Goldman, 2006). (Yang and Lozano-Perez, 2000) and (Zhang et al, 2002) were among the first to apply MIL to image retrieval, which led to more subsequent work on this topic (Zhang et al, 2006; Zhu et al, 2006).

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