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Spring Lattice Counting Grids: Scene Recognition Using Deformable Positional Constraints

  • Alessandro Perina
  • Nebojsa Jojic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

Adopting the Counting Grid (CG) representation [1], the Spring Lattice Counting Grid (SLCG) model uses a grid of feature counts to capture the spatial layout that a variety of images tend to follow. The images are mapped to the counting grid with their features rearranged so as to strike a balance between the mapping quality and the extent of the necessary rearrangement. In particular, the feature sets originating from different image sectors are mapped to different sub-windows in the counting grid in a configuration that is close, but not exactly the same as the configuration of the source sectors. The distribution over deformations of the sector configuration is learnable using a new spring lattice model, while the rearrangement of features within a sector is unconstrained. As a result, the CG model gains a more appropriate level of invariance to realistic image transformations like view point changes, rotations or scales. We tested SLCG on standard scene recognition datasets and on a dataset collected with a wearable camera which recorded the wearer’s visual input over three weeks. Our algorithm is capable of correctly classifying the visited locations more than 80% of the time, outperforming previous approaches to visual location recognition. At this level of performance, a variety of real-world applications of wearable cameras become feasible.

Keywords

Spring Lattice Latent Dirichlet Allocation Visual Stream Scene Recognition Indoor Scene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alessandro Perina
    • 1
  • Nebojsa Jojic
    • 1
  1. 1.Microsoft ResearchRedmondUSA

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