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Generic Object Recognition. A Project on. -- by Yatharth Saraf. Problem Definition and Background. Recognizing generic class or category of a given object as opposed to recognizing specific, individual objects

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generic object recognition

Generic Object Recognition

A Project on

-- by Yatharth Saraf

problem definition and background
Problem Definition and Background
  • Recognizing generic class or category of a given object as opposed to recognizing specific, individual objects
    • humans are much better at generic recognition, machines are more competitive at specific object recognition
  • Early work by Marr led to the ‘reconstruction school’
    • advocates 3-D reconstruction and modeling before further reasoning of a scene
  • Current work in object categorization tends to fall in the ‘recognition school’
    • work in the 2-D domain, with 2-D image features and descriptors
    • e.g. Bag of features approaches, spatial 2-D geometry approaches as in the ‘constellation model’
applications
Applications
  • Image database annotation and retrieval
  • Video surveillance
  • Driver assistance, autonomous robots
  • Cognitive support for disabled people
related work
Related Work
  • Discriminative approaches
    • SVM, subspace methods
  • Bag of features
    • Representation of objects with point descriptors
  • Constellation model
    • Representations that take into account spatial geometry (2-D) of key points
assumptions
Assumptions
  • Images are scale-normalized
  • Images are clean, i.e. no background clutter/occlusion
    • (-) Implies segmentation is necessary as a pre-processing step
    • (+) Avoids the problem of exponential search
outline of the method training
Outline of the Method (Training)
  • Detect salient regions in all training images using Kadir-Brady feature detector
  • Extract X,Y coordinates, scale and 11x11 intensity patches around detected features
  • Reduce dimensionality of appearance patches from 121 to 16 using PCA
  • Estimate model parameters
    • A single full Gaussian for location; one Gaussian per part
outline of the method testing
Outline of the Method (Testing)
  • Extract features of test images in the same manner as in training phase
  • Use the learnt model to estimate probability of detection
  • Use Bayes’ Decision Rule to classify
experiments
Experiments
  • Careful tweaking of detector parameters needed
  • A single set of parameter settings may not be suitable for all categories
slide9

Starting scale: 3

Starting scale: 23

experiments contd
Experiments (contd.)
  • 47 clean motorbike images used for training motorbike model
  • Sorting the extracted patches by X-coordinate helped (as opposed to sorting by saliency)
  • Appearance model not doing as well
slide12

Log-probabilities of the 9 test images from location model

Features sorted by X-coordinate.

Features sorted by saliency.

Image 5

Image 9

slide13

Appearance log-probabilities of the 9 test images

Features sorted by saliency.

Features sorted by X-coordinate.

Total log-probabilities of the 9 test images

experiments contd14
Experiments (contd.)
  • Using a Mixture of Gaussians for the appearances of parts didn’t make too much difference

3 mixture components per part (EM initialized with k-means and sample covariances)

experiments contd15
Experiments (contd.)
  • Levenshtein distances on the appearance patches worked quite nicely
  • Each appearance patch is a single character
  • Matching cost was computed using a straight SSD
  • Cost of inserting a gap = matching cost of the patch with a canonical 11x11 patch having uniform intensity of 128.
conclusions and future work
Conclusions and Future Work
  • Strong dependence on feature detector
  • Appearance model doesn’t seem to be working too well
  • Levenshtein distances could be more promising
  • Experiments with more clean training and test data, multiple categories
  • Exponential search for dealing with clutter and occlusion
questions

Questions?

-- Thank You