Efficient Subwindow Search: A Branch and Bound Framework for Object Localization ‘PAMI09 Beyond Sliding Windows: Object Localization by Efficient Subwindow Search The best paper prize at CVPR 2008

Motivation To localize the object without exhaustive search observation : often, only a small portion of the image contains the object of interest To find a global optimum in a huge search space Branching and bounding Object detection and retrieval

SVM: Localization problem SVM answers ‘Yes’ or ‘No’ to whether the objects belongs to the classifier’s object class as well as returns confidence score It cannot say where the object is located in the image and at what scale

SVM Object Localization Methods Exhaustive Search. For n x n image complexity is O(n 4 ) Sliding Window Approach

Branch–and–Bound Scheme Branching. Dividing a space of candidate rectangles into subspaces Bounding. Pruning subspaces with a highest possible score lower than some guaranteed score in other subspaces

Bounding function To use branch-and-bound for given quality function f, we need to define upper bound function

Algorithm

Example I. Bag of visual words SVM For every image Extract SIFT image descriptors Quantize descriptors using K-entry codebook of descriptors Represent an image by a histogram of codebook entry occurences every image is coded as 1-dimensional vector h of length K where K is the number of codebook ‘words’

Example I. Bounding function SVM Decision function: We can express it as a sum of per-point contributions with weights If we denote by R max the largest rectangle and by R min the smallest rectangle contained in a parameter region R, then

Example I. Experiment PASCAL VOC 06 5,304 images with 9,507 objects from 10 categories 1000 visual words from 50,000 SURF descriptors claim a match when > 50% overlap between the detected bounding box and the ground truth PASCAL VOC ,963 images with 24,640 objects

Recall Precision Curve

Example II. Spatial Pyramid Kernel SVM

SVM Decision function: We can express it as a sum of per-point contributions with weights The upper bound for f is obtained by summing the bounds for all levels and cells Consider this as Down Sampling

Example II. Experiment UIUC Car database (side-view, one car per image) 1050 training (550 positive images) 277 test (170 single scale multi scale) 1000 visual words from 50,000 SURF descriptors

Example III. Nonlinear More Quality bounds by interval arithmetic

Example III. Experiment keyframes of a movie return 100 most relevant images for a query 2s per returned image

Experiments

Summary Fast Global Optimal Easy to extend (change classifiers, parametric space) Future Kernel-based Classifiers Extensions (groups of boxes, circles …_)