Filter object categories using CoBoost with 1ST and 2ND order features

We describe a method for filtering object category from a large number of noisy images. This problem is particularly difficult due to the greater variation within object categories and lack of labeled object images. Our method deals with it by combining a co-training algorithm CoBoost with two features - 1^st and 2^nd order features, which define bag of words representation and spatial relationship between local features respectively. We iteratively train two boosting classifiers based on the 1^st and 2^nd order features, during which each classifier provides labeled data for the other classifier. It is effective because the 1^st and 2^nd order features make up an independent and redundant feature split. We evaluate our method on Berg dataset and demonstrate the precision comparative to the state-of-the-art.