Spatial-bag-of-features

In this paper, we study the problem of large scale image retrieval by developing a new class of bag-of-features to encode geometric information of objects within an image. Beyond existing orderless bag-of-features, local features of an image are first projected to different directions or points to generate a series of ordered bag-of-features, based on which different families of spatial bag-of-features are designed to capture the invariance of object translation, rotation, and scaling. Then the most representative features are selected based on a boosting-like method to generate a new bag-of-features-like vector representation of an image. The proposed retrieval framework works well in image retrieval task owing to the following three properties: 1) the encoding of geometric information of objects for capturing objects´ spatial transformation, 2) the supervised feature selection and combination strategy for enhancing the discriminative power, and 3) the representation of bag-of-features for effective image matching and indexing for large scale image retrieval. Extensive experiments on 5000 Oxford building images and 1 million Panoramio images show the effectiveness and efficiency of the proposed features as well as the retrieval framework.