Abstract:
Visual feature clustering is one of the cost-effective
approaches to segment objects in videos. However, the
assumptions made for developing the existing algorithms
prevent them from being used in situations like segmenting
an unknown number of static and moving objects under
heavy camera movements. This paper addresses the problem
by introducing a clustering approach based on superpixels and
short-term Histogram of Oriented Optical Flow (HOOF). Salient
Dither Pattern Feature (SDPF) is used as the visual feature to
track the flow and Simple Linear Iterative Clustering (SLlC) is
used for obtaining the superpixels. This new clustering approach
is based on merging superpixels by comparing short term local
HOOF and a color cue to form high-level semantic segments. The
new approach was compared with one of the latest feature
clustering approaches based on K-Means in eight-dimensional
space and the results revealed that the new approach is better by
means of consistency, completeness, and spatial accuracy.
Further, the new approach completely solved the problem of not
knowing the number of objects in a scene.
