GDL

An Intelligent Model for Visual Scene Analysis and Compression

2010
Journal from gdlhub / 2017-08-14 11:52:32
Oleh : Amjad Rehman, Tanzila Saba, IAJIT
Dibuat : 2012-06-23, dengan 1 file

Keyword : Visualization, discrete cosine transform, image compression, scene analysis.
Subjek : An Intelligent Model for Visual Scene Analysis and Compression
Url : http://www.ccis2k.org/iajit/PDF/vol.10,no.2/8-2977.pdf
Sumber pengambilan dokumen : Internet

This paper presents an improved approach for indicating visually salient regions of an image based upon a known


visual search task. The proposed approach employs a robust model of instantaneous visual attention (i.e. “bottom-up”)


combined with a pixel probability map derived from the automatic detection of a previously-seen object (task-dependent i.e.


(“top-down”). The objects to be recognized are parameterized quickly in advance by a viewpoint-invariant spatial distribution


of Speeded Up Robust Features (SURF) interest-points. The bottom-up and top-down object probability images are fused to


produce a task-dependent saliency map. The proposed approach is validated using observer eye-tracker data collected under


object search-and-count tasking. Proposed approach shows 13% higher overlap with true attention areas under task


compared to bottom-up saliency alone. The new combined saliency map is further used to develop a new intelligent


compression technique which is an extension of Discrete Cosine Transform (DCT) encoding. The proposed approach is


demonstrated on surveillance-style footage throughout.

Deskripsi Alternatif :

This paper presents an improved approach for indicating visually salient regions of an image based upon a known


visual search task. The proposed approach employs a robust model of instantaneous visual attention (i.e. “bottom-up”)


combined with a pixel probability map derived from the automatic detection of a previously-seen object (task-dependent i.e.


(“top-down”). The objects to be recognized are parameterized quickly in advance by a viewpoint-invariant spatial distribution


of Speeded Up Robust Features (SURF) interest-points. The bottom-up and top-down object probability images are fused to


produce a task-dependent saliency map. The proposed approach is validated using observer eye-tracker data collected under


object search-and-count tasking. Proposed approach shows 13% higher overlap with true attention areas under task


compared to bottom-up saliency alone. The new combined saliency map is further used to develop a new intelligent


compression technique which is an extension of Discrete Cosine Transform (DCT) encoding. The proposed approach is


demonstrated on surveillance-style footage throughout.

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