  Semantic signal processing and communications are poised to play a central
part in developing the next generation of sensor devices and networks. A
crucial component of a semantic system is the extraction of semantic signals
from the raw input signals, which has become increasingly tractable with the
recent advances in machine learning (ML) and artificial intelligence (AI)
techniques. The accurate extraction of semantic signals using the
aforementioned ML and AI methods, and the detection of semantic innovation for
scheduling transmission and/or storage events are critical tasks for reliable
semantic signal processing and communications. In this work, we propose a
reliable semantic information extraction framework based on our previous work
on semantic signal representations in a hierarchical graph-based structure. The
proposed framework includes a time integration method to increase fidelity of
ML outputs in a class-aware manner, a graph-edit-distance based metric to
detect innovation events at the graph-level and filter out sporadic errors, and
a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The
proposed methods within the framework are demonstrated individually and
collectively through simulations and case studies based on real-world computer
vision examples.
