Object Localization using Adaptive Feature Selection

摘要

'Fast and robust' are the most beautiful keywords in computer vision. Unfortunately they are in trade-off relationship. We present a method to have one's cake and eat it using adaptive feature selections. Our chief insight is that it compares reference patterns to query patterns, so that it selects smartly more important and useful features to find target. The probabilities of pixels in the query to belong to the target are calculated from importancy of features. Our framework has three distinct advantages: 1 - It saves computational cost dramatically to the conventional approach. This framework makes it possible to find location of an object in real-time. 2 - It can smartly select robust features of a reference pattern as adapting to a query pattern. 3- It has high flexibility on any feature. It doesn't matter which feature you may use. Lots of color space, texture, motion features and other features can fit perfectly only if the features meet histogram criteria.