Visual Object Recognition

@inproceedings{Grauman2011VisualOR,
  title={Visual Object Recognition},
  author={Kristen Grauman and B. Leibe},
  booktitle={Visual Object Recognition},
  year={2011}
}
The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in… Expand
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References

SHOWING 1-10 OF 362 REFERENCES
Object categorization by learned universal visual dictionary
TLDR
An optimally compact visual dictionary is learned by pair-wise merging of visual words from an initially large dictionary, and a novel statistical measure of discrimination is proposed which is optimized by each merge operation. Expand
OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning
  • Li-Jia Li, Li Fei-Fei
  • Computer Science
  • 2007 IEEE Conference on Computer Vision and Pattern Recognition
  • 2007
TLDR
This paper presents a novel object recognition algorithm that performs automatic dataset collecting and incremental model learning simultaneously, and adapts a non-parametric latent topic model and proposes an incremental learning framework. Expand
Context-based vision system for place and object recognition
TLDR
A low-dimensional global image representation is presented that provides relevant information for place recognition and categorization, and it is shown how such contextual information introduces strong priors that simplify object recognition. Expand
Sub-linear Indexing for Large Scale Object Recognition
TLDR
A method capable of recognising one of N objects in log(N) time, which preserves all the strengths of local affine region methods – robustness to background clutter, occlusion, and large changes of viewpoints. Expand
Multiple Component Learning for Object Detection
TLDR
The method, Multiple Component Learning (mcl), automatically learns individual component classifiers and combines these into an overall classifier, and unlike methods that are not part-based, mcl is quite robust to occlusions. Expand
Real-time 100 object recognition system
TLDR
A real-time vision system is described that can recognize 100 complex three-dimensional objects and its recognition rate was found to be 100% and object pose was estimated with a mean absolute error of 2.02 degrees and standard deviation of 1.67 degrees. Expand
The Pascal Visual Object Classes (VOC) Challenge
TLDR
The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse. Expand
Towards automatic discovery of object categories
TLDR
A method to learn heterogeneous models of object classes for visual recognition that automatically identifies distinctive features in the training set and learns the set of model parameters using expectation maximization. Expand
Incremental learning of object detectors using a visual shape alphabet
TLDR
A visual alphabet representation which can be learnt incrementally, and explicitly shares boundary fragments and spatial configurations across object categories, and shows that category similarities can be predicted from the alphabet. Expand
Object recognition from local scale-invariant features
  • D. Lowe
  • Mathematics, Computer Science
  • Proceedings of the Seventh IEEE International Conference on Computer Vision
  • 1999
TLDR
Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds. Expand
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