Title: Multiple Object Class Detection with a Generative Model
1 Multiple Object Class Detection with a Generative Model
K. Mikolajczyk B. Leibe and B. Schiele
Carolina Galleguillos 2 Goal
Simultaneous recognition and localization of multiple object classes using a generative model.
Recognition Codebook (features are shared among several object classes).
Detection Probabilistic model for various objects in the same image.
3 Introduction Multiple Object class detection performance is far from single object. Single Object class detection is a mature problem
Fast and dense sampling of scale invariant features.
Effective object representation.
Efficient and reliable training and recognition.
Based on feature detectors
Local features several detectors.
Based on appearance clusters
Visual vocab. codebook keywords.
Represent object classes
Star shape graphical model etc.
5 Features - Appearance
We can compute them efficiently
Scale space pyramid with a Gaussian kernel.
For each level Canny edge detection with Laplacian automatic scale (position scale and dominant orientation).
For each edge point we identify a region of interest (in the gradient orientation). This region is described by SIFT descriptors (128 dimensional vector).
Use of PCA for dimensionality reduction (to 40 dimensions).
6 Features - Geometry
Rotation invariance Convert position of features in polar coordinates.
d distance to object center. f angle. dominant gradient orientation. 7 Hierarchical Codebook
Hierarchical tree of clusters Appearance clusters (formed by similar features at first level) Each cluster has several geometric distributions that correspond to object classes (info about geom. relations between object centers and local appearance). Node is a hyperball 8 Building Tree Efficiently
Apply K-means to divide space (top-down).
Use reciprocal nearest neighbor in each k-means partition with a similarity threshold.
Apply agglomerative clustering (bottom up).
Euclidean distance to group clusters.
Clustering trace is used to construct the tree.
9 Building Tree Efficiently 10 Building Tree Efficiently 11 Building Tree Efficiently 12 Building Tree Efficiently 13 Building Tree Efficiently 14 Tree - Advantages
Appearance clusters are shared within one image and among different classes (and object parts).
Represent individual objects or all object classes.
Bayesian rule approach
F features. A appearance clusters. G geometric distribution. Decision Each feature likelihood is modeled by a mixture of distributions from appearance clusters which match to a query feature. 16 Recognition
Problem Similar objects in the model have probabilities comparables in shared clusters.
Condition each feature can contribute only to one hypothesis.
Average confusion factor between pairs of objects.
If approaches to 1 we remove from both hypothesis all info that come from those clusters.
Joint probability distributions are separated in two terms
To estimate de model
Extract features F from labeled training examples. Build appearance clusters match the features back to the cluster centers (threshold ß). Each feature that matches to contributes to the prob. estimates for the appearance and to its geometric distrib. at the position. 18 Fast Matching
Match features to cluster centers using a ball tree.
Represent query and model as tree structures.
Match two trees computing Euclidean distance between centroids of top nodes.
If distance is smaller than the sum of their radii then the first node is compared with all the children of the intersecting node. Same precision to exhaustive search and 200 times faster. 19 Experimental results 5 object classes pedestrian cars motorbikes bicycles and RPG shooter. 20 Experimental results Recall is higher and the number of appearance clusters grow sub-linearly with increasing number of object classes Motorbike test data 21 Conclusions
Approach capable of detecting multiple object classes simultaneously in images using a single codebook.
Performance comparable with state of the art discriminative approaches.
Efficient method for building object class representation and recognition.
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