Receptive Fields Selection for Binary Feature Description

Feature description for local image patch is widely used in computer vision. While the conventional way to design local descriptor is based on expert experience and knowledge, learning-based methods for designing local descriptor become more and more popular because of their good performance and data-driven property. This paper proposes a novel data-driven method for designing binary feature descriptor, which we call receptive fields descriptor (RFD). Technically, RFD is constructed by thresholding responses of a set of receptive fields, which are selected from a large number of candidates according to their distinctiveness and correlations in a greedy way. Using two different kinds of receptive fields (namely rectangular pooling area and Gaussian pooling area) for selection, we obtain two binary descriptors RFDR and RFDG accordingly. Image matching experiments on the well-known patch data set and Oxford data set demonstrate that RFD significantly outperforms the state-of-the-art binary descriptors, and is comparable with the best float-valued descriptors at a fraction of processing time. Finally, experiments on object recognition tasks confirm that both RFDR and RFDG successfully bridge the performance gap between binary descriptors and their floating-point competitors.

[1]  Kristen Grauman,et al.  Kernelized locality-sensitive hashing for scalable image search , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[2]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[3]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Gang Hua,et al.  Discriminative Learning of Local Image Descriptors , 1990, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[6]  Trevor Darrell,et al.  Learning to Hash with Binary Reconstructive Embeddings , 2009, NIPS.

[7]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Hongping Cai,et al.  Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  David J. Fleet,et al.  Minimal Loss Hashing for Compact Binary Codes , 2011, ICML.

[10]  Raj Gupta,et al.  Robust order-based methods for feature description , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[12]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Raj Gupta,et al.  SMD: A Locally Stable Monotonic Change Invariant Feature Descriptor , 2008, ECCV.

[14]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[15]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Ethan Rublee,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[17]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[18]  Svetlana Lazebnik,et al.  Iterative quantization: A procrustean approach to learning binary codes , 2011, CVPR 2011.

[19]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[21]  Vincent Lepetit,et al.  Boosting Binary Keypoint Descriptors , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Zhanyi Hu,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Rotationally Invariant Descript , 2011 .

[23]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[24]  Pascal Fua,et al.  LDAHash: Improved Matching with Smaller Descriptors , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Adrien Bartoli,et al.  KAZE Features , 2012, ECCV.

[26]  Matti Pietikäinen,et al.  IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2009, TPAMI-2008-09-0620 1 WLD: A Robust Local Image Descriptor , 2022 .

[27]  Matthew A. Brown,et al.  Picking the best DAISY , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Gregory Shakhnarovich,et al.  Learning task-specific similarity , 2005 .

[29]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Hai Tao,et al.  A novel feature descriptor invariant to complex brightness changes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[32]  Rongrong Ji,et al.  Supervised hashing with kernels , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Vincent Lepetit,et al.  Efficient Discriminative Projections for Compact Binary Descriptors , 2012, ECCV.

[34]  Boris Babenko,et al.  Task Specific Local Region Matching , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[35]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[36]  Antonio Torralba,et al.  Spectral Hashing , 2008, NIPS.

[37]  Shih-Fu Chang,et al.  Sequential Projection Learning for Hashing with Compact Codes , 2010, ICML.

[38]  Bin Fan,et al.  Local Intensity Order Pattern for feature description , 2011, 2011 International Conference on Computer Vision.

[39]  Matti Pietikäinen,et al.  RLBP: Robust Local Binary Pattern , 2013, BMVC.

[40]  Richard Szeliski,et al.  Building Rome in a day , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[41]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[42]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[43]  Shih-Fu Chang,et al.  Semi-supervised hashing for scalable image retrieval , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[44]  Andrew Zisserman,et al.  Descriptor Learning Using Convex Optimisation , 2012, ECCV.

[45]  Vincent Lepetit,et al.  Learning Image Descriptors with the Boosting-Trick , 2012, NIPS.

[46]  Sanjiv Kumar,et al.  Learning Binary Codes for High-Dimensional Data Using Bilinear Projections , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[47]  Steven M. Seitz,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[48]  Zhanyi Hu,et al.  Aggregating gradient distributions into intensity orders: A novel local image descriptor , 2011, CVPR 2011.

[49]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .