Abstract:
Salient region detection intends to find visually important and noticeable regions in visual
scenes; as such regions hold significant information and straightforwardly capture human
attention. Identified salient objects can be later used in more complex computer vision and
image processing applications such as object detection, recognition and tracking, image retrieval,
content based image editing & cropping, and image compression. Salient object
detection faces challenge in uniformly highlighting desired objects and suppressing irrelevant
background. Due to rapid development in technology, wide number of techniques
have been proposed for saliency detection, yet there is a need for an effective and reliable
saliency detection technique. Existing heuristic methods acquire false detection while dealing
with complex scenarios i.e. cluttered backgrounds or foreground object camouflaged in
background.
In this thesis, an effective framework is proposed to improve the salient region detection
in complex scenarios. The contrast of image is enhanced using weighted approximated
histogram equalization as pre-processing step. Edge preserving guided filter is used to minimize
the unwanted details (texture) while maintaining the edges and semantics. Iterative
rolling guidance filter is applied to perform scale-aware local operations for image abstraction.
Cellular automata is then used to obtain and optimize saliency cues by exploiting local
similarity. A cost reduction framework is further employed to integrate low level cues in
order to produce cleaner saliency maps.
Proposed technique effectively deals with problems found in saliency detection and produce
accurate saliency maps in challenging scenarios where existing techniques fail to claim
sharp boundaries of salient object. Visual and quantitative comparisons with state of art
existing techniques verify the significance of proposed technique. Salient regions detected
by proposed technique can be further used in many image processing areas including object
recognition and tracking, image retrieval, automatic cropping and image compression.
