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Spatiotemporal Saliency Detection and Its Applications in Static and Dynamic Scenes
IEEE TCSVT 2011Wonjun Kim
Chanho Jung
Changick Kim
Proposed Method
Feature RepresentationEdge orientation histogram (EOH)Color orientation histogram (COH)Temporal Feature
Self-ordinal MeasureSaliency MapScale-invariant Saliency Map
Edge Orientation Histogram (EOH)
1. Compute the edge orientation of every pixel in the local region center at the pixel
2. Quantized into K angle in the range of [,]
3. Compute the histogram of edge orientation
m(x,y,n):edge magnitude(x,y,n):quantized orientation
𝑖
local region
Color Orientation Histogram (COH)
1. Quantize the angle in HSV color space in the range of [,] into H angles
2. Compute the histogram of color orientation
s(x,y,n):saturation value(x,y,n):quantized hue value
Temporal Feature
Compute the intensity differences between frames
Feature at the pixel of frame
P :total number of pixels in local regionj :index of those pixels in P :user-defined latency
Saliency Map of Edge and Color
Compute the distance from the rank matrix of center region to surrounding regions
Saliency Map of Edge Saliency Map of Color
N :total number of local regions in a center-surround window
, :maximum distance between two rank matrices
Combining with Temporal Saliency
Compute the SAD of temporal gradients between center and the surrounding regions
Combine the spatial and temporal saliency
Scale-invariant Saliency Map
Combine 3 different scales of saliency Map(3232, 6464, 128128)
3232 1281286464
Experiment Result
Static ImageLocal region = 55center-surround window = 77K = 8, H= 6 = 40, = 24
Video Sequence = 49Speed: 23ms per frame (43 fps)
Moving Object Detection
G:the set of salient pixels in the ground truth imageP:salient pixels in the binarized object mapCard(A):the size of the set A