Improving Efficient Subwindow Search in Object Localization
Date Issued
2010
Date
2010
Author(s)
Wang, Tse-Yi
Abstract
Object detection and localization is one of the most important studies in the field of computer vision, allowing for the detection of natural objects in a myriad of consumer electronic systems such as photograph archives or video databases. Object detection answers the question of whether a certain object of interest is present inside an image while object localization deals with the more difficult problem of where an object exists inside an image.
For the task of object localization a common implementation is to represent the image as a 2-dimensional array of object contribution values. This transforms the localization problem into maximal sub-array search where the objective is to find the highest scoring sub-array which represents the location of the object in question. To this end an exhaustive search method called sliding windows search has been proposed and recently a more efficient method based on branch and bound search called efficient subwindow search has gained popularity.
Often times it is the accurateness of the visual words which form basis of object contribution score array that is the bottleneck for localization performance. With our multi-box intersection method we could locate the position of an object within an image even if there is considerable amount of noise within the image feature array. Multi-box intersection first finds composite bounding boxes over different sampling frequencies which tend to give a good estimate of where the searched object lies. Then we reduce the amount of signal noise through intersection of the obtained composite boxes. By doing so we could obtain better localization results than previous single bounding box approaches which we demonstrate in our experiments.
Subjects
object localization
subwindow search
Type
thesis
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