Location recognition based on image local feature matching

Location identification is a research hot spot in computer vision. For a scene with the building, the location recognition method in this paper can accurately detect the building and identify the location. The specific method is to firstly determine the local feature extraction method to obtain more stable local features under different conditions. Secondly, encode image features effectively, build Scene codebook, establish image index, and compare image similarity. Fast and large-scale image retrieval can be achieved in this way. Then, in order to filter out the error matching results and choose the best matching result, a matching algorithm based on local spatial consistency is proposed. The shape model voting method with small calculation amount is proposed to obtain the position of the building in the scene picture. The experimental results show that the method can more accurately identify the location of the building, and the building images show good robustness and distinguishability when they are transformed.

[1]  Majid Ahmadi,et al.  Parallel randomized KD-tree forest on GPU cluster for image descriptor matching , 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

[2]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Jan Kybic,et al.  Approximate Best Bin First k-d Tree All Nearest Neighbor Search with Incremental Updates , 2010 .

[4]  David G. Lowe,et al.  Shape indexing using approximate nearest-neighbour search in high-dimensional spaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Bruce R. Rosen,et al.  Image reconstruction by domain-transform manifold learning , 2017, Nature.

[6]  Florent Perronnin,et al.  Fisher Kernels on Visual Vocabularies for Image Categorization , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Lan Wang,et al.  Face recognition based on PCA image reconstruction and LDA , 2013 .

[8]  Jean-Arcady Meyer,et al.  Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words , 2008, IEEE Transactions on Robotics.

[9]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[10]  Yang Li,et al.  Combining Nonlinear Dimension Reduction and Hashing Method for Efficient Image Retrieval , 2016, 2016 12th International Conference on Semantics, Knowledge and Grids (SKG).

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

[12]  Vittorio Murino,et al.  Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification , 2016, ArXiv.

[13]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.