Special issue on multi-modal information learning and analytics for smart city
- PDF / 312,315 Bytes
- 2 Pages / 595.276 x 790.866 pts Page_size
- 28 Downloads / 162 Views
EDITORIAL
Special issue on multi‑modal information learning and analytics for smart city Zheng Xu1 · Qingyuan Zhou2
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
We are living in the era of data deluge. Meanwhile, the world of big data exhibits a rich and complex set of cross-media contents, such as text, image, video, audio and graphics. Thus far, great research efforts have been separately dedicated to big data processing and cross-media mining, with well theoretical underpinnings and great practical success. However, studies jointly considering cross-media big data analytics are relatively sparse. This research gap needs our more attention, since it will benefit lots of real-world applications. Despite its significance and value, it is non-trivial to analyze cross-media big data due to their heterogeneity, large-scale volume, increasing size, unstructured, correlations, and noise. Multi-modal Information Learning, which can be treated as the most significant breakthrough in the past 10 years, has greatly affected the methodology of computer vision and achieved terrific progress in both academy and industry. From then on, deep learning has been adopted in all kinds of computer vision applications and many breakthroughs have achieved in sub-areas, like DeepFace on LFW competition for face verification, GoogleNet for ImageNet Competition for object categorization. It can be expected that more and more computer vision applications will benefit from Multi-modal Information Learning. The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. Totally, we have received 27 manuscripts and 14 papers are accepted in this SI. This special issue will publish by Journal of Ambient Intelligence and Humanized Computing as special issues.
* Zheng Xu [email protected] 1
Shanghai University, 99 Shangda Road, Shanghai 201142, China
Changzhou Institute of Mechatronic Technology, Changzhou 213164, Jiangsu, China
2
The improved neural network is used by Long (2019) to extract the trajectory characteristics of the athletes in the football player’s game video, and the network is trained on a large number of data objects containing similarity objects, which improves the ability of the algorithm to distinguish the athlete’s trajectory. Li et al. (2019) proposes an intrinsic image decomposition method based on depth learning and probability graph model, in order to extract image information more accurately. Zhao et al. (2019) construct four logistics forums metrics on the degree of busyness and emotional states of the logistics staffs based through hypertext analysis techniques. Liu et al. (2019) employ the VRP with different maturities and the ADL-MIDAS regression model to forecast the expected stock return in Standard & Poor 500 market. To improve the performance, a neutral mutation (NM) operator for DE algorithm is proposed by Ma et al. (2019). The proposed operator is inspired by ne
Data Loading...
