融合相位一致性與二維主成分分析的視覺顯著性預(yù)測(cè)

徐威; 唐振民

doi:10.11999/JEIT141478

融合相位一致性與二維主成分分析的視覺顯著性預(yù)測(cè)

doi: 10.11999/JEIT141478

徐威¹,
唐振民¹

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61473154)

計(jì)量
- 文章訪問(wèn)數(shù): 1386
- HTML全文瀏覽量: 114
- PDF下載量: 654
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2014-11-24
- 修回日期: 2015-03-11
- 刊出日期: 2015-09-19

Integrating Phase Congruency and Two-dimensional Principal Component Analysis for Visual Saliency Prediction

Xu Wei¹,
Tang Zhen-min¹

摘要

摘要: 為了更加有效地預(yù)測(cè)圖像中吸引視覺注意的關(guān)鍵區(qū)域，該文提出一種融合相位一致性與2維主成分分析(2DPCA)的顯著性方法。該方法不同于傳統(tǒng)的利用相位譜的方式，而是提出采用相位一致性(PC)獲取圖像中重要的特征點(diǎn)和邊緣信息，經(jīng)快速漂移超像素優(yōu)化后，融合局部和全局顏色對(duì)比度，生成低層特征顯著圖。接著提出利用2DPCA提取圖像塊的主成分后，計(jì)算主成分空間中圖像塊的局部和全局可區(qū)分性，得到模式顯著圖。最后，通過(guò)空間離散度度量分配合適的權(quán)重，使兩者融合，提取顯著性區(qū)域。在兩種人眼跟蹤數(shù)據(jù)庫(kù)上與5種經(jīng)典算法的實(shí)驗(yàn)對(duì)比結(jié)果表明，該算法能更加準(zhǔn)確地預(yù)測(cè)人眼視覺關(guān)注點(diǎn)。
- 圖像處理 /
- 視覺顯著性 /
- 人眼關(guān)注點(diǎn)預(yù)測(cè) /
- 相位一致性 /
- 2維主成分分析
Abstract: In order to predict the pivotal visually attractive image regions more effectively, a novel saliency method using the phase congruency and the two-Dimensional Principal Component Analysis (2DPCA) is proposed in this paper. Firstly, the phase congruency is utilized to extract the most important feature points and the edge informations in the frequency domain, which is different from the conventional phase spectrum based methods. Then, after the quick shift superpixel based refinement, these features are incorporated with the local and global color contrast, to generate the low-level feature based saliency map. Then, the 2DPCA is adopted to extract the principal component vectors of image patches. The local and global distinctness between the different image patches in the principal component space are computed to get the pattern saliency map. Finally, these two complementary maps are integrated through the weighting strategy based on the spatial variance measure. The comparable experimental results on two benchmark eye tracking databases of the proposed method and 5 state-of-the-art methods show that the proposed method can predict eye fixation more accurately.
- Image processing /
- Visual saliency /
- Eye fixation prediction /
- Phase congruency /
- Two-Dimensional Principal Component Analysis (2DPCA)

HTML全文

參考文獻(xiàn)(18)

Li W T, Chang H S, Lien K C, et al.. Exploring visual and motion saliency for automatic video object extraction[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2600-2610.

Chen D Y and Luo Y S. Preserving motion-tolerant contextual visual saliency for video resizing[J]. IEEE Transactions on Multimedia, 2013, 15(7): 1616-1627.

Borji A, Sihite D N, and Itti L. Quantitative analysis of human-model agreement in visual saliency modeling: a comparative study[J]. IEEE Transactions on Image Processing, 2013, 22(1): 55-69.

Itti L, Koch C, and Niebur E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.

Harel J, Koch C, and Perona P. Graph-based visual saliency [C]. Proceedings of the Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 2007: 545-552.

Bruce N D and Tsotsos J K. Saliency based on information maximization[C]. Proceedings of the Annual Conference on Neural Information Processing Systems, Whistler, Canada, 2006: 155-162.

Borji A and Itti L. Exploiting local and global rarities for saliency detection[C]. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 478-485.

Judd T, Ehinger K, and Durand F. Learning to predict where humans look[C]. Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan, 2009: 2106-2113.

Vig E, Dorr M, and David C. Large-scale optimization of hierarchical features for saliency prediction in natural images [C]. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 2798-2805.

Hou X and Zhang L. Saliency detection: a spectral residual approach[C]. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 2007: 1-8.

Li J, Levine M D, An X J, et al.. Visual saliency based on scale-space analysis in the frequency domain[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(4): 996-1010.

Yang J, Zhang D, Frangi A F, et al.. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137.

Jiang H Z, Wu Y, and Yuan Z J. Probabilistic salient object contour detection based on superpixels[C]. Proceedings of the IEEE International Conference on Image Processing, Melbourne, Australia, 2013: 3069-3072.

Kovesi P. Phase congruency detects corners and edges[C]. Proceedings of the Australian Pattern Recognition Society Conference, Sydney, Australia, 2003: 309-318.

Vedaldi A and Soatto S. Quick shift and kernel methods for mode seeking[C]. Proceedings of the European Conference on Computer Vision, Marseille, France, 2008: 705-718.

Wei Y C, Wen F, and Zhu W J. Geodesic saliency using background priors[C]. Proceedings of the European Conference on Computer Vision, Florence, Italy, 2012: 29-42.

Cheng M M, Warrell J, Lin W Y, et al.. Efficient salient region detection with soft image abstraction[C]. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia, 2013: 1529-1536.

Shi T L, Liang M, and Hu X L. A reverse hierarchy model for predicting eye fixations[C]. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 23-28.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問(wèn)統(tǒng)計(jì)