recognition using sift features

Recognition Using SIFT Features

CS491Y/691Y Topics in Computer Vision Dr. George Bebis

Object Recognition

• Model-based Object Recognition

• Generic Object Recognition

Model-Based Object Recognition

• Recognition relies upon the existence of a set of predefined objects.

Generic Object Recognition (or Object Categorization)

Feature-based Recognition

(1) Identify a group of features from an unknown scene which approximately match a group of features from a model object (i.e., correspondences).

(2) Recover the geometric transformation that the model object has undergone and look for additional matches.

model scene

2D Transformation Spaces

• Rigid transformations (3 parameters)

• Similarity transformations (4 parameters)

• Affine transformations (6 parameters)

Matching – Main Steps• Hypothesis generation: the identities of one or more models

are hypothesized.• Hypothesis verification: tests are performed to check if a

given hypothesis is correct or not.

Recognition

Hypothesis Verification - Example

Features might correspond to:

(1) curvature extrema orzero-crossings alongthe boundary of anobject .

(2) interest points.

two hypotheses:

Object Recognition using SIFT features

1. Match individual SIFT features from an image to a database of SIFT features from known objects (i.e., find nearest neighbors).

2. Find clusters of SIFT features belonging to a single object (hypothesis generation).

Object Recognition using SIFT features

3. Estimate object pose (i.e., recover the transformation that the model has undergone) using at least three matches.

4. Verify that additional features agree on object pose (hypothesis verification).

Nearest neighbor search

• Linear search: too slow for large database • kD trees: become slow when k > 10

Nearest neighbor search (cont’d)

• Approximate nearest neighbor search:– Best-bin-first [Beis et al. 97] (modification to kD-tree algorithm)– Examine only the N closest bins of the kD-tree– Use a heap to identify bins in order by their distance from query.

• Can give speedup by factor of 1000 while finding nearest neighbor (of interest) 95% of the time.

Marius Muja and David G. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration", International Conference on Computer Vision Theory and Applications, 2009

FLANN - Fast Library for Approximate Nearest Neighbors

Estimate object pose

• Now, given feature matches…– Find clusters of features corresponding to a single object.– Solve for transformation (e.g., affine transformation).

model scene

Estimate object pose (cont’d)

• Need to consider clusters of size >=3• How do we find three “good” (i.e., true) matches?

Estimate object pose (cont’d)• Pose clustering

– Each SIFT feature is associated with four parameters: – For every match (mi, sj), estimate a similarity

transformation between mi and sj

(2D location, scale, orientation)(2D location, scale, orientation)

(tx,ty,s,θ)

vote

Estimate object pose (cont’d)

– Transformation space is 4D: (tx, ty, s, θ)

(tx,ty,s,θ) (t’x,t’y,s’,θ’) ….

votes

Estimate object pose (cont’d)

– Partial voting: vote for neighboring bins and use

large bin size to better tolerate errors.

– Transformations that accumulate at least three votes are selected (hypothesis generation).

– Using model-scene matches, compute object pose (i.e., affine transformation) and apply verification.

Verification• Back-project model (i.e., interest points) on the scene and

look for additional matches.

• Discard outliers (i.e., incorrect matches) by imposing stricter matching constraints (e.g., half error).

• Find additional matches and refine the transformation computed (i.e., iterative affine refinements).

• Repeat until no additional matches can be found.

Verification (cont’d)• Additional verification: evaluate probability that match

is correct.

– Use Bayesian (probabilistic) model, to estimate the probability that a model is present based on the matching features.

– Bayesian model takes into account: • Object size in image• Textured regions• Model feature count in database• Accuracy of fit

Lowe, D.G. 2001. Local feature view clustering for 3D object recognition.IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, pp. 682–688.

Planar recognition

Models

Planar recognition (cont’d)

• Reliably recognized at a rotation of 60° away from the camera.

• Affine fit approximates perspective projection.

• Only 3 points are needed for recognition.

Test image

3D object recognitionModels

3D object recognition

• Only 3 keys are needed for recognition; extra keys provide robustness.

• Affine model is no longer as accurate.

Test images

Recognition under occlusion

Object Categorization

Bag-of-features (BoF) model

Origin 1: Texture recognition• Texture is characterized by the repetition of basic elements

or textons.• Many times, it is the identity of the textons, not their

spatial arrangement, that matters.

Origin 1: Texture recognition

Universal texton dictionary

histogram

Universal texton dictionary

histogram

Origin 2: Document retrieval• Orderless document representation: frequencies of words from a dictionary Salton & McGill (1983)

BoF for object categorization

G. Cruska et al., "Visual Categorization with Bags of Keypoints", European Conference on Computer Vision, Czech Republic, 2004

Need a “visual” dictionary!

BoF: main steps

Characterize objects in terms of parts or local features

BoF: main steps

Step 1: Feature extraction (e.g., SIFT features)

…

BoF: main steps (cont’d)

Step 2: Learn “visual” vocabulary

…

“visual” vocabulary

Feature extraction & clustering

BoF: main steps (cont’d)

Clustering

…

BoF: main steps (cont’d)

Clustering

…

“Visual” vocabulary: cluster centers

Example: K-means clustering

•Want to minimize sum of squared Euclidean distances between points xi and their nearest cluster centers mk

Example: K-means clustering

Algorithm:•Randomly initialize K cluster centers•Iterate until convergence:

– Assign each data point to the nearest center.– Re-compute each cluster center as the mean of all points

assigned to it.

More powerful clustering algorithms

•Affinity propagation http://www.psi.toronto.edu/index.php?q=affinity%20propagation

•Autoclasshttp://ti.arc.nasa.gov/tech/rse/synthesis-projects-applications/autoclass/autoclass-c/

BoF: main steps (cont’d)Step 3: Quantize features using “visual” vocabulary (i.e., represent each feature by the closest cluster center).

…

BoF: main steps (cont’d)

Step 4: Represent images by frequencies of “visual words” (i.e., bags of features)

BoF Object Categorization

• How do we use BoF for object categorization?

BoF Object Categorization (cont’d)

(1) Use a Nearest Neighbor (NN) Classifier

BoF Object Categorization (cont’d)

• Functions for comparing histograms

BoF Object Categorization (cont’d)

(2) Use a K-Nearest Neighbor (KNN) Classifier

Find the k closest points from training data.

Labels of the k points “vote” to classify.

Works well provided there is lots of data and the distance function is good.

BoF Object Categorization (cont’d)

(3) Naïve Bayes classifier

BoF Object Categorization (cont’d)

(3) Naïve Bayes classifier

BoF Object Categorization (cont’d)

(3) Naïve Bayes classifier

BoF Object Categorization (cont’d)

(3) Naïve Bayes classifier

or

BoF Object Categorization (cont’d)

(4) Use an SVM classifier

SVM

SVM

SVM

ExampleCaltech6 dataset

Example (cont’d)Caltech6 dataset

Dictionary quality and size are very important parameters!

What about spatial information?

Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce, "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories", CVPR 2006.

What about spatial information?Scene category dataset

100 training images per class