Digital Signal Processing Reference
In-Depth Information
Fig. 2
Semantic concept detection in applications
1.1.1
Application 1: Semantic Concept Detection in Multimedia;
Processing Heterogeneous and Dynamic Data
in a Resource-Constrained Setting
Figure
2
illustrates how stream mining can be used to tag concepts on images
or videos in order to perform a wide set of tasks, from search to ad-targeting.
Based upon this stream mining framework, designers can construct, instrument,
experiment with, and optimize applications that automatically categorize image and
video data captured by various cameras into a list of semantic concepts (e.g., skating,
tennis, etc.) using various chains of classifiers.
Importantly, such stream mining systems need to be highly adaptive to the
dynamic and time-varying multimedia sequence characteristics, since the input
stream is highly volatile. Furthermore, they must often be able to cope with limited
system resources (e.g. CPU, memory, I/O bandwidth), working on devices such
as smartphones with increasing power restrictions. Therefore, applications need
to cope effectively with system overload due to large data volumes and limited
system resources. Commonly used approaches to dealing with this problem in
resource constrained stream mining are based on load-shedding, where algorithms
determine when, where, what, and how much data to discard given the observed
data characteristics, e.g. burst, desired Quality of Service (QoS) requirements, data
value or delay constraints.
1.1.2
Application 2: Online Healthcare Monitoring; Processing Data
in Real Time
Monitoring individual's health requires handling a large amount of data, coming
from multiple sources such as biometric sensor data or contextual data sources.
