Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

Question

1. What are the contributions in "Beyond bags of features: spatial pyramid matching for recognizing natural scene categories" ?

2. What is the method for capturing information about objects?

3. What is the way to capture the organization of major pictorial elements?

4. What is the method for detecting people and bikes?

Accepted Answer

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence.. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region.

Accepted Answer

because of the geometric stability and lack of clutter of Caltech-101, dense features combined with global spatial relations seem to capture more discriminative information about the objects.

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In particular, spatial pyramids seem successful at capturing the organization of major pictorial elements or “blobs,” and the directionality of dominant lines and edges.

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the authors train detectors for persons and bikes on 100 positive and 100 negative images (of which 50 are drawn from the other object class and 50 from the background), and test on a similarly distributed set.

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The behavior of weak features on this database is also noteworthy: for L = 0, they give a classification rate of 15.5%, which is consistent with a naive graylevel correlation baseline [1], but in conjunction with a four-level spatial pyramid, their performance rises to 54% — on par with the best results in the literature.

Accepted Answer

The least successful classes are either textureless animals (like beaver and cougar), animals that camouflage well in their environment3See, however, H. Zhang et al. [24] in these proceedings, for an algorithm that yields a classification rate of 66.2 ± 0.5% for 30 training examples, and 59.1 ± 0.6% for 15 examples.(like crocodile), or “thin” objects (like ant).

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For maximum computational efficiency, the authors normalize all histograms by the total weight of all features in the image, in effect forcing the total number of features in all images to be the same.

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More specifically, let us construct a sequence of grids at resolutions 0, . . . , L, such that the grid at level has 2 cells along each dimension, for a total of D = 2d cells.

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As expected, weak features do not perform as well as strong features, though in combination with the spatial pyramid, they can also achieve acceptable levels of accuracy (note that because weak features have a much higher density and much smaller spatial extent than strong features, their performance continues to improve as the authors go from L = 2 to L = 3).

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Their decision to use a dense regular grid instead of interest points was based on the comparative evaluation of Fei-Fei and Perona [4], who have shown that dense features work better for scene classification.

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Their retrieval experiments (Fig. 4) confirm that spatial pyramids can capture perceptually salient features and suggest that “locally orderless matching” may be a powerful mechanism for estimating overall perceptual similarity between images.

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To verify this, the authors have experimented with probabilistic latent semantic analysis (pLSA) [9], which attempts to explain the distribution of features in the image as a mixture of a few “scene topics” or “aspects” and performs very similarly to LDA in practice [17].

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Their classification rate is 72.2% (74.7% for the 13 classes inherited from Fei-Fei and Perona), which is much higher than their best results of 65.2%, achieved with an orderless method and a feature set comparable to ours.

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Their method involves repeatedly subdividing the image and computing histograms of local features at increasingly fine resolutions.

Accepted Answer

In principle, it is possible to integrate geometric information directly into the original pyramid matching framework by treating image coordinates as two extra dimensions in the feature space.

Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

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Most frequently asked questions

1. What are the contributions in "Beyond bags of features: spatial pyramid matching for recognizing natural scene categories" ?

2. What is the method for capturing information about objects?

3. What is the way to capture the organization of major pictorial elements?

4. What is the method for detecting people and bikes?

5. What is the classification rate for weak features?

6. What are the difficult objects to classify?

7. What is the way to normalize the features in the image?

8. How many cells are in a grid at level?

9. What is the difference between weak features and strong features?

10. What is the main reason why the authors chose to use a dense regular grid instead of interest points?

11. What is the main difference between spatial pyramids and multiresolution histograms?

12. What is the way to explain the distribution of features in an image?

13. How is the classification rate for the 13 classes?

14. What is the method used to compute histograms of local features?

15. How can the authors integrate geometric information into the pyramid matching framework?

Figures

Citations

ImageNet Large Scale Visual Recognition Challenge

Fast R-CNN

ImageNet Large Scale Visual Recognition Challenge

The Pascal Visual Object Classes (VOC) Challenge

Fast R-CNN

References

Bidirectional Reflection Distribution Function of Thoroughly Pitted Surfaces

Biologically Motivated Computer Vision: Second International Workshop

Weak Hypotheses and Boosting for Generic Object Detection and Recognition

The Structure of Locally Orderless Images

Recognition with local features: the kernel recipe

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