Computer Science ›› 2019, Vol. 46 ›› Issue (3): 303-313.doi: 10.11896/j.issn.1002-137X.2019.03.045

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Multitask Hierarchical Image Retrieval Technology Based on Faster RCNNH

HE Xia, TANG Yi-ping, WANG Li-ran, CHEN Peng, YUAN Gong-ping   

  1. (School of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
  • Received:2018-02-02 Revised:2018-06-22 Online:2019-03-15 Published:2019-03-22

Abstract: Aiming at the problems of low-level automation and intelligence,lack of deep learning,being difficult to obtain high retrieval accuracy,large storage space,slow retrieval speed and hardly meeting the search requirements of big data era for the existing search technologies,this paper proposed a multitask hierarchical image retrieval technology based on faster RCNNH(Faster RCNN Hash).Firstly,the logical regression is performed on the feature map by using the selective retrieval network to obtain the probability vectors of each region of interest in the image.On this basis,the compact quantization network is combined to encode the probability vector and obtain the compact and quantitative hash of the image.Secondly,the re-screening network is utilized to obtain the region-aware semantic features of each region of interest.Then,a precise search strategy based on quantitative hashing matrix is applied into each region of interest to compare the images fast.Finally,the image that is most similar to the corresponding region of interest in the query ima-ge is selected.Meanwhile,the proposed multitask learning method not only can simultaneously obtain compact and quantized hash codes and region-aware semantic features,but also can effectively remove the interference of the background and other objects.The experimental results show that the proposed method can achieve end-to-end training,and the network can automatically select the features with higher quality of the region of interest,thereby improving the automation and intelligence of large-scale image retrieval. The retrieval accuracy (0.9478) and search speed (0.306ks) of the proposed method are both significantly better than the existing large-scale image search technologies.

Key words: Deep hash algorithm, Hash code, Large-scale image retrieval, Multitask deep learning, Region of interest

CLC Number: 

  • TP391.4
[1]SMEULDERS A W M,WORRING M,SANTINI S,et al.Content-based image retrieval at the end of the early years[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2000,22(12):1349-1380.
[2]WAN J,WANG D,HOI S C H,et al.Deep Learning for Content-Based Image Retrieval:A Comprehensive Study[C]∥Acm International Conference on Multimedia.ACM,2014:157-166.
[3]YUAN J,ZHENG Y,ZHANG C,et al.An interactive-voting
based map matching algorithm[C]∥Proceedings of the 2010 Eleventh International Conference on Mobile Data Management.IEEE Computer Society,2010:43-52.
[4]BAY H,TUYTELAARS T,GOOL L V.SURF:Speeded Up
Robust Features[J].Computer Vision & Image Understanding,2006,110(3):404-417.
[5]QIU G.Indexing chromatic and achromatic patterns for content-based colour image retrieval[J].Pattern Recognition,2002,35(8):1675-1686.
[6]HAYKIN S,KOSKO B.Gradient Based Learning Applied to
Document Recognition[M].New York:Wiley-IEEE Press.2009:306-351.
[7]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet
classification with deep convolutional neural networks[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc.,2012:1097-1105.
[8]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Net-
works for Large-Scale Image Recognition[J].Computer Scien-ce,2014.arxiv:1409.1556
[9]GIONIS A,INDYK P,MOTWANI R.Similarity Search in High Dimensions via Hashing[C]∥International Conference on Very Large Data Bases.Morgan Kaufmann Publishers Inc.,2000:518-529.
[10]WEISS Y,TORRALBA A,FERGUS R.Spectral Hashing[C]∥
Proceedings of the Twenty-second Annual Conference on Neural Information Processing Systems.Curran Associates Inc.,2008.
[11]CHANG S F.Supervised hashing with kernels[C]∥IEEE Con-
ference on Computer Vision and Pattern Recongnition.2012.
[12]GONG Y,LAZEBNIK S.Iterative quantization:A procrustean approach to learning binary codes[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE ComputerSocie-ty,2011:817-824.
[13]KULIS B,GRAUMANK.Kernelized locality-sensitive hashing
for scalable image search[C]∥IEEE International Conference on Computer Vision.IEEE,2009:2130-2137.
[14]XIA R,PAN Y,LAI H,et al.Supervised hashing for image retrieval via image representation learning[C]∥AAAI Conference on Artificial Intelligence.2014.
[15]LI J Y,LI J H.Supervised hashing binary code with deep CNN for image retrieval[C]∥International Conference on Biomedical Engineering and Informatics.2015:649-655.
[16]LAI H,PAN Y,LIU Y,et al.Simultaneous feature learning and hash coding with deep neural networks[C]∥IEEE Conference on Computer Vision and Patter Recongnition.2015:3270-3278.
[17]LIN K,YANG H F,HSIAO J H,et al.Deep learning of binary hash codes for fast image retrieval[C]∥Computer Vision and Pattern Recognition Workshops.IEEE,2015:27-35.
[18]REN S Q,HE K M,GIRSHICK R,et al.Faster r-cnn:Towards real-time object detection with region proposal networks [C]∥Advances in neural information processing systems (NIPS).Palais des Congrès de Montréal:2015:91-99.
[19]OLIVA A,TORRALBA A.Chapter 2 Building the gist of a
scene:the role of global image features in recognition[J].Progress in Brain Research,2006,155(2):23.
[20]DENG J,DONG W,SOCHER R,et al.ImageNet:A large-scale hierarchical image database[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2009(CVPR 2009).IEEE,2009:248-255.
[21]RAGINSKY M.Locality-Sensitive Binary Codes from Shift-In-
variant Kernels[J].Advances in Neural Information Processing Systems,2009:1509-1517.
[22]GONG Y,LAZEBNIK S.Iterative quantization:A procrustean approach to learning binary codes[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Socie-ty,2011:817-824.
[23]YU F X,KUMAR S,GONG Y,et al.Circulant Binary Embedding[J].Computer Science,2014:946-954.arxiv:1405.3162
[24]BERG T,LIU J,LEE S W,et al.Birdsnap:Large-Scale Fine-Grained Visual Categorization of Birds[C]∥Computer Vision and Pattern Recognition.IEEE,2014:2019-2026.
[25]WEISS Y,TORRALBA A,FERGUS R.Spectral hashing[C]∥International Conference on Neural InformationProcessing Systems.Curran Associates Inc.2008:1753-1760.
[26]JIN Z,LI C,LIN Y,et al.Density sensitive hashing[J].IEEE Transactions on Cybernetics,2012,44(8):1362-1371.
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