JOURNAL ARTICLE

Improved seam carving for image retargeting with sift feature preservation

Abstract

This paper presents an effective and simple image resizing method. Our method is an improved version of seam carving that changes the backtracking basis of seam carving. We use a Scale Invariant Feature Transform (SIFT) feature in our method. SIFT key points are mainly located on high-contrast regions of an image. By using saliency considering SIFT as our backtracking basis, a resized image can preserve the SIFT features of the original image. Therefore, the resized image can be more visually acceptable than the image resized by traditional seam carving.

Keywords:
Seam carving Scale-invariant feature transform Artificial intelligence Computer vision Computer science Backtracking Feature (linguistics) Image (mathematics) Carving Pattern recognition (psychology) Engineering Algorithm

Metrics

3
Cited By
0.42
FWCI (Field Weighted Citation Impact)
14
Refs
0.68
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Visual Attention and Saliency Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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