Saliency Detection for Content-aware Image Resizing

Radhakrishna Achanta and Sabine Susstrunk

Abstract

Content aware image re-targeting methods aim to arbitrarily change image aspect ratios while preserving visually prominent features. To determine visual importance of pixels, existing re-targeting schemes mostly rely on grayscale intensity gradient maps. These maps show higher energy only at edges of objects, are sensitive to noise, and may result in deforming salient objects. We present a computationally efficient, noise robust re-targeting scheme based on seam carving by using saliency maps that assign higher importance to visually prominent whole regions (and not just edges). This is achieved by computing global saliency of pixels using intensity as well as color features. Our saliency maps easily avoid artifacts that conventional seam carving generates and are more robust in the presence of noise.

Publication

Results

In each row, from left to right:
1. The original image
2. Intensity gradient map
3. Our saliency map
4. Conventional seam carving result
5. Result using our saleiency map.

(a) Original	(b) Energy map	(c) 80% width	(d) 120% width
0	0	0	0
1	1	1	1
2	2	2	2
3	3	3	3
4	4	4	4
5	5	5	5
6	6	6	6
7	7	7	7
8	8	8	8
9	9	9	9
10	10	10	10
11	11	11	11
12	12	12	12
13	13	13	13
14	14	14	14
15	15	15	15
16	16	16	16
17	17	17	17
18	18	18	18
19	19	19	19
20	20	20	20
21	21	21	21

Acknowledgements

This work is supported by National Competence Center in Research on Mobile Information and Communication Systems (NCCR-MICS), a center supported by the Swiss National Science Foundation under grant number 5005-67322, and by K-Space, the European Network of Excellence in Knowledge Space of semantic inference for automatic annotation and retrieval of multimedia content.