Image matching in large scale indoor environment

In this paper, we propose a data driven approach to first-person vision. We propose a novel image matching algorithm, named Re-Search, that is designed to cope with self-repetitive structures and confusing patterns in the indoor environment. This algorithm uses state-of-art image search techniques, and it matches a query image with a two-pass strategy. In the first pass, a conventional image search algorithm is used to search for a small number of images that are most similar to the query image. In the second pass, the retrieval results from the first step are used to discover features that are more distinctive in the local context. We demonstrate and evaluate the Re-Search algorithm in the context of indoor localization, with the illustration of potential applications in object pop-out and data-driven zoom-in.

[1]  Kui-Lam Kwok,et al.  A new method of weighting query terms for ad-hoc retrieval , 1996, SIGIR '96.

[2]  Christos Faloutsos,et al.  MindReader: Querying Databases Through Multiple Examples , 1998, VLDB.

[3]  Prabhakar Raghavan,et al.  Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies , 1998, The VLDB Journal.

[4]  Christos Faloutsos,et al.  FALCON: Feedback Adaptive Loop for Content-Based Retrieval , 2000, VLDB.

[5]  Mario A. Nascimento,et al.  A compact and efficient image retrieval approach based on border/interior pixel classification , 2002, CIKM '02.

[6]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Mounia Lalmas,et al.  A survey on the use of relevance feedback for information access systems , 2003, The Knowledge Engineering Review.

[8]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[9]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[10]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[11]  Trevor Darrell,et al.  The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[12]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[13]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[15]  Andrew Zisserman,et al.  Video Google: Efficient Visual Search of Videos , 2006, Toward Category-Level Object Recognition.

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

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

[18]  Jitendra Malik,et al.  Image Retrieval and Classification Using Local Distance Functions , 2006, NIPS.

[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.  A contextual dissimilarity measure for accurate and efficient image search , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Jianbo Shi,et al.  Image Matching via Saliency Region Correspondences , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Michael Isard,et al.  Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[23]  Cordelia Schmid,et al.  Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search , 2008, ECCV.

[24]  Andrew Zisserman,et al.  Near Duplicate Image Detection: min-Hash and tf-idf Weighting , 2008, BMVC.

[25]  Andrew Zisserman,et al.  Object Mining Using a Matching Graph on Very Large Image Collections , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[26]  Andrew Zisserman,et al.  Efficient Visual Search for Objects in Videos , 2008, Proceedings of the IEEE.

[27]  Alexei A. Efros,et al.  IM2GPS: estimating geographic information from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Michael Isard,et al.  Lost in quantization: Improving particular object retrieval in large scale image databases , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .