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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
TLDR
This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. Expand
Learning realistic human actions from movies
TLDR
A new method for video classification that builds upon and extends several recent ideas including local space-time features,space-time pyramids and multi-channel non-linear SVMs is presented and shown to improve state-of-the-art results on the standard KTH action dataset. Expand
Action Recognition with Improved Trajectories
  • Heng Wang, C. Schmid
  • Mathematics, Computer Science
  • IEEE International Conference on Computer Vision
  • 1 December 2013
TLDR
Dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets are improved by taking into account camera motion to correct them. Expand
A Performance Evaluation of Local Descriptors
TLDR
It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors. Expand
Aggregating local descriptors into a compact image representation
TLDR
This work proposes a simple yet efficient way of aggregating local image descriptors into a vector of limited dimension, which can be viewed as a simplification of the Fisher kernel representation, and shows how to jointly optimize the dimension reduction and the indexing algorithm. Expand
Scale & Affine Invariant Interest Point Detectors
TLDR
A comparative evaluation of different detectors is presented and it is shown that the proposed approach for detecting interest points invariant to scale and affine transformations provides better results than existing methods. Expand
Product Quantization for Nearest Neighbor Search
This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and toExpand
Action recognition by dense trajectories
TLDR
This work introduces a novel descriptor based on motion boundary histograms, which is robust to camera motion and consistently outperforms other state-of-the-art descriptors, in particular in uncontrolled realistic videos. Expand
A Comparison of Affine Region Detectors
TLDR
A snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework. Expand
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
TLDR
Estimation of the full geometric transformation of bag-of-features in the framework of approximate nearest neighbor search is complementary to the weak geometric consistency constraints and allows to further improve the accuracy. Expand
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