A risk probability-map generation model on multimedia services environment

Kim, Dong-Hyun; Kim, Jin-Mook; Jeong, Young‐Sik; Park, Koo-Rack

doi:10.1007/s11042-014-2441-0

Cited by 1 publication

(1 citation statement)

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“…This research detects facial the facial region and major facial features by Haar-like feature and AdaBoost learning algorithm. Finally, Jin-Mook Kim et al [17] suggested a novel model of combined risk probability map generation to predict crime frequency. It analyzes risk in collective residential areas using urban spatial information, and then two risk probability maps are generated by the information based on terrain.…”

Section: Related Workmentioning

confidence: 99%

Guest Editorial: Large-scale Multimedia Data Management: Techniques and Applications

Hung

Takizawa

Chen

2016

Multimed Tools Appl

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Essential to many tasks in relation to multimedia research and development is the availability of a sufficiently large data set and its corresponding ground truth. However, most available data for multimedia research are either too specific, e.g., data for text retrieval; too small, e.g., face figures; nor without ground truth, such as gathering millions of un-preprocessing images from the Web for testing. While it is relatively easy to crawl and store a huge amount of data, the creation of ground truth necessary to systematically train, test, evaluate, and compare the performance of various algorithms and systems remains a challenging issue. For this reason, researchers tend to put (or redirect) efforts into the creation of such corpus individually to carry out research on large-scale data sets. Thus, a promising trend of a united web-scale and distributed multimedia data management is urgently needed, which would benefit the entire multimedia research community. This special issue presents and reports on the construction and analysis of large-scale multimedia data sets and resources, and provides a strong reference for multimedia researchers interested in large-scale multimedia data sets. In particular, the special issue demonstrates the emerging techniques and applications for large-scale multimedia data management.

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Section: Related Workmentioning

confidence: 99%