Abstract
We propose a gradient-based global feature and its application to seam carving. We focus on areas, rather than points and lines, to be assigned as important elements for expressing the rough location of salient objects in an image. The proposed feature is calculated with a low computational load based on gray-scale intensity. The superior performance of the proposed gradient-based global feature, as compared to state-of-the-art salient features for seam carving, is demonstrated experimentally.
Highlights
The goal of image retargeting is to resize an image with a different aspect ratio while preserving salient objects
The gradient-based global feature represents the rough locations of salient objects in an image, and the seam locations obtained using multiple layers are better than those obtained using a single layer
5.1 Deformation on specific zones The results obtained using the proposed feature were compared with the results obtained using the gradient magnitude (GM), the balanced energy map (BEM) [16], the prohibited points (PP) [17], and the context-aware saliency (CAS) [19] (We used the MATLAB implementation from [24])
Summary
The goal of image retargeting is to resize an image with a different aspect ratio while preserving salient objects. We address the problem whereby important objects expressed as smoother areas are distorted in seam carving. The proposed method is based on the simple idea that, rather than points and lines, areas that include salient objects in an image should be given importance and is motivated by the histogram of oriented gradients (HoG) [20] and the scale-invariant feature transform (SIFT) [21, 22], which are well-established feature descriptors. The proposed method does not require making histograms like HoG and SIFT but using the inverse variance of weighted gradient orientation. Gradient-based local features, the central idea of which is the use of a weighted gradient orientation histogram of a local area.
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