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n-words模型下Hesse稀疏表示的圖像檢索算法

王瑞霞 彭國華

王瑞霞, 彭國華. n-words模型下Hesse稀疏表示的圖像檢索算法[J]. 電子與信息學(xué)報, 2016, 38(5): 1115-1122. doi: 10.11999/JEIT150617
引用本文: 王瑞霞, 彭國華. n-words模型下Hesse稀疏表示的圖像檢索算法[J]. 電子與信息學(xué)報, 2016, 38(5): 1115-1122. doi: 10.11999/JEIT150617
WANG Ruixia, PENG Guohua. Hesse Sparse Representation under n-words Model for Image Retrieval[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1115-1122. doi: 10.11999/JEIT150617
Citation: WANG Ruixia, PENG Guohua. Hesse Sparse Representation under n-words Model for Image Retrieval[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1115-1122. doi: 10.11999/JEIT150617

n-words模型下Hesse稀疏表示的圖像檢索算法

doi: 10.11999/JEIT150617
基金項目: 

國家自然科學(xué)基金(61201323)

Hesse Sparse Representation under n-words Model for Image Retrieval

Funds: 

The National Natural Science Foundation of China (61201323)

  • 摘要: 論文針對視覺詞袋(BOVW)模型放棄圖像空間結(jié)構(gòu)的缺點,提出一種基于Hesse稀疏編碼的圖像檢索算法。首先,建立n-words模型,獲得圖像局部特征表示。n-words模型由一系列連續(xù)視覺詞獲得,是圖像特征的一種高級描述。該文從n=1到n=5進行試驗,尋找最恰當(dāng)?shù)膎值;其次,將二階Hesse能量函數(shù)融入標(biāo)準(zhǔn)稀疏編碼的目標(biāo)函數(shù),得到Hesse稀疏編碼公式;最后,以獲得的n-words序列作為編碼特征,利用特征符號搜索算法求解最優(yōu)Hesse系數(shù),計算相似度,返回檢索結(jié)果。實驗在兩類數(shù)據(jù)集上進行,與BOVW模型和已有的算法相比,新算法極大地提高了圖像檢索的準(zhǔn)確率。
  • SIVIC J and ZISSERMAN A. Video google: A text retrieval approach to object matching in videos[C]. Proceedings of the Ninth IEEE International Conference on Computer Vision, Nice, France, 2003: 1470-1477. doi: 10.1109/ICCV.2003. 1238663.
    LAZEBNIK S, SCHMID C, and PONCE J. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, USA, 2006: 2169-2178. doi: 10.1109/CVPR.2006. 68.
    ZHANG Shiliang, TIAN Qi, HUA Gang, et al. Generating descriptive visual words and visual phrases for large-scale image applications[J]. IEEE Transactions on Image Processing, 2011, 20(9): 2664-2677. doi: 10.1109/TIP. 2011. 2128333.
    CHEN Tao, YAP Kimhui, and ZHANG Dajiang. Discriminative bag-of-visual phrase learning for landmark recognition[C]. Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto, Japan, 2012: 893-896. doi: 10.1109/ICASSP.2012. 6288028.
    YANG Meng, ZHANG Lei, YANG Jian, et al. Robust sparse coding for face recognition[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Colorado, Springs, USA, 2011: 625-632. doi: 10.1109/CVPR.2011.5995393.
    LIU Weifeng, TAO Dacheng, CHENG Jun, et al. Multiview Hessian discriminative sparse coding for image annotation[J]. Computer Vision and Image Understanding, 2014, 118: 50-60. doi: 10.1016/j.cviu.2013.03.007.
    REDDY M K, TALUR J, and BABU R V. Sparse coding based VLAD for efficient image retrieval[C]. Proceedings of the 2014 IEEE International Conference on Electronics, Computing and Communication Technologies, Bangalore, India, 2014: 1-4. doi: 10.1109/CONECCT.2014.6740340.
    LIU Qiegen, YING Leslie, and LIANG Dong. An efficient augmented Lagrangian algorithm for graph regularized sparse coding in clustering[C]. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada, 2013: 1656-1660. doi: 10. 1109/ICASSP.2013.6637933.
    錢智明, 鐘平, 王潤生. 基于圖正則化與非負組稀疏的自動圖像標(biāo)注[J]. 電子與信息學(xué)報, 2015, 37(4): 784-790. doi: 10. 11999/JEIT141282.
    QIAN Zhiming, ZHONG Ping, and WANG Runsheng. Automatic image annotation via graph regularization and non-negative group sparsity[J]. Journal of Electronics Information Technology, 2015, 37(4): 784-790. doi: 10.11999/ JEIT141282.
    劉哲, 楊靜, 陳路. 基于非局部稀疏編碼的超分辨率圖像復(fù)原[J]. 電子與信息學(xué)報, 2015, 37(3): 522-528. doi: 10.11999/ JEIT140481.
    LIU Zhe, YANG Jing, and CHEN Lu. Super-resolution image restoration based on nonlocal sparse coding[J]. Journal of Electronics Information Technology, 2015, 37(3): 522-528. doi: 10.11999/JEIT140481.
    YANG Jianchao, YU Kai, GONG Yihong, et al. Linear spatial pyramid matching using sparse coding for image classification[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida, USA, 2009: 1794-1801. doi: 10.1109/CVPRW.2009.5206757.
    WANG Jinjun, YANG Jianchao, YU Kai, et al. Locality- constrained linear coding for image classification[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, California, USA, 2010: 3360-3367. doi: 10.1109/CVPR.2010.5540018.
    GAO Shenghua, TSANG Ivor WaiHung, and CHIA Liangtien. Laplacian sparse coding, hypergraph Laplacian sparse coding, and applications[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 92-104. doi: 10.1109/ TPAMI.2012.63.
    PEDROSA G V and TRAINA A J M. From bag-of-visual- words to bag-of-visual-phrases using n-grams[C]. Proceedings of the 2013 XXVI Conference on Graphics, Patterns and Images, Arequipa, Peru, 2013: 304-311. doi: 10.1109/ SIBGRAPI.2013.49.
    SUEN C Y. N-gram statistics for natural language understanding and text processing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1978, 1(2): 164172.
    ZHENG Miao, BU Jiajun, and CHEN Chun. Hessian sparse coding[J]. Neurocomputing, 2014, 123: 247-254. doi: 10.1016/ j.neucom.2013.08.001.
    LEE H, BATTLE A, RAINA R, et al. Efficient sparse coding algorithms[C]. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2007: 801-808.
    KIM K, STEINKE F, and HEIN M. Semi-supervised regression using Hessian energy with an application to semi-supervised dimensionality reduction[C]. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, Canada, 2009: 979-987.
    LI Fefei, ROB F, and PIETRO P. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories[J]. Computer Vision and Image Understanding, 2007, 106: 59-70. doi: 10.1016/j. cviu.2005.09.012.
    POWERS D M W. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness correlation [J]. Journal of Machine Learning Technologies, 2011, 2(1): 37-63.
    TURPIN A and SCHOLER F. User performance versus precision measures for simple search tasks[C]. Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Washington, USA, 2006: 11-18.
    王瑞霞, 彭國華, 鄭紅嬋. 拉普拉斯稀疏編碼的圖像檢索算法[J]. 計算機科學(xué), 2014, 41(8): 278-280. doi: 10.11896/j.issn. 1002-137X.2014.08.058.
    WANG Ruixia, PENG Guohua, and ZHENG Hongchan. Image retrieval algorithm based on Laplacian sparse coding [J]. Computer Science, 2014, 41(8): 278-280. doi: 10.11896/ j.issn.1002-137X.2014.08.058.
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出版歷程
  • 收稿日期:  2015-05-20
  • 修回日期:  2016-01-15
  • 刊出日期:  2016-05-19

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