How important is location information in saliency detection of natural images

被引:0
作者
Tongwei Ren
Yan Liu
Ran Ju
Gangshan Wu
机构
[1] Nanjing University,State Key Laboratory for Novel Software Technology
[2] The Hong Kong Polytechnic University,Department of Computing
来源
Multimedia Tools and Applications | 2016年 / 75卷
关键词
Saliency detection; Location information; Patch representation; Saliency propagation;
D O I
暂无
中图分类号
学科分类号
摘要
Location information, i.e., the position of content in image plane, is considered as an important supplement in saliency detection. The effect of location information is usually evaluated by integrating it with the selected saliency detection methods and measuring the improvement, which is highly influenced by the selection of saliency methods. In this paper, we provide direct and quantitative analysis of the importance of location information for saliency detection in natural images. We firstly analyze the relationship between content location and saliency distribution on four public image datasets, and validate the distribution by simply treating location based Gaussian distribution as saliency map. To further validate the effectiveness of location information, we propose a location based saliency detection approach, which completely initializes saliency maps with location information and propagate saliency among patches based on color similarity, and discuss the robustness of location information’s effect. The experimental results show that location information plays a positive role in saliency detection, and the proposed method can outperform most state-of-the-art saliency detection methods and handle natural images with different object positions and multiple salient objects.
引用
收藏
页码:2543 / 2564
页数:21
相关论文
共 82 条
[1]  
Bao B-K(2012)Hidden-concept driven multilabel image annotation and label ranking IEEE Trans Multimed 14 199-210
[2]  
Li T(2013)State-of-the-art in visual attention modeling IEEE Transactions on Pattern Analysis and Machine Intelligence 35 185-207
[3]  
Yan S(2013)Quantitative analysis of human-model agreement in visual saliency modeling: a comparative study IEEE Trans Image Process 22 55-69
[4]  
Borji A(2014)Salientshape: group saliency in image collections Vis Comput 30 443-453
[5]  
Itti L(2015)Global contrast based salient region detection IEEE Transactions on Pattern Analysis and Machine Intelligence 37 409-416
[6]  
Borji A(1997)Visual image retrieval by elastic matching of user sketches IEEE Transactions on Pattern Analysis and Machine Intelligence 19 121-132
[7]  
Sihite DN(2013)Depth really matters. Improving visual salient region detection with depth British Machine Vision Conference 98 1-11
[8]  
Itti L(2012)Context-aware saliency detection IEEE Transactions on Pattern Analysis and Machine Intelligence 34 1915-1926
[9]  
Cheng M-M(1998)A model of saliency-based visual attention for rapid scene analysis IEEE Transactions on Pattern Analysis and Machine Intelligence 20 1254-1259
[10]  
Mitra NJ(2011)Automatic salient object segmentation based on context and shape prior British Machine Vision Conference 3 1-7