8/2/2019 slide: Object Class Recognition Using Discriminative Local Features http://slidepdf.com/reader/full/slide-object-class-recognition-using-discriminative-local-features 1/17 Object Class Recognition Using Discriminative Local Features Gyuri Dorko and Cordelia Schmid
17
Embed
slide: Object Class Recognition Using Discriminative Local Features
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
8/2/2019 slide: Object Class Recognition Using Discriminative Local Features
With the clustering set positive descriptors are obtained to estimate aGaussian Mixture Model (GMM). It is a parametric estimation of theof the probability distribution of the local descriptors.
Where K is the number of Gaussian components and:
The dimension of the vectors x is 128 corresponding to the dimensions of theSIFT features.
8/2/2019 slide: Object Class Recognition Using Discriminative Local Features
The model parameters mi, Si and P(Ci ) are computed with theexpectation-maximization (EM) algorithm. The EM is initialized with the output of the K-means algorithm. This are the equations toupdate the parameters at the jth maximization (M) step.
8/2/2019 slide: Object Class Recognition Using Discriminative Local Features
The clusters are obtained from assigning each descriptor to its closestcomponent. The clusters typically contain representative object parts or textures.
Here we see some characteristicclusters of each database.
With the mixture model a boundary is defined for each component toform K part classifiers . Each classifieris associated with one Gaussian
A test feature y is assigned to thecomponent i* having the highestprobability.
8/2/2019 slide: Object Class Recognition Using Discriminative Local Features
Initial step for localization within images. Theoutput is not binary but a ranking of the partclassification.
Classification of the presence or absence of anobject in an image. Here is required anadditional criterion of how many p positive classified descriptors are required to mark thepresence of an object. The authors uses thisbecause it is easier to compare.
8/2/2019 slide: Object Class Recognition Using Discriminative Local Features