Pyramid intersection is an efficient method for computing an approximate partial matching
between two sets of feature vectors. We introduce a novel pyramid embedding based on a …

As the commonly used representation of a feature-rich data object has evolved from a single
feature vector to a set of feature vectors, a key challenge in building a content-based search …

In numerous domains it is useful to represent a single example by the set of the local
features or parts that comprise it. However, this representation poses a challenge to many …

Discriminative learning is challenging when examples are sets of features, and the sets vary
in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods …

In visual recognition, the images are frequently modeled as sets of local features (bags). We
show that bag of words, a common method to handle such cases, can be viewed as a …

Object recognition can be formulated as matching image features to model features. When
recognition is exemplar-based, feature correspondence is one-to-one. However …

Similarity measures in many real applications generate indefinite similarity matrices. In this
paper, we consider the problem of classification based on such indefinite similarities. These …

In this paper we propose a global optimization-based approach to jointly matching a set of
images. The estimated correspondences simultaneously maximize pairwise feature affinities …

Spatial pyramid matching (SPM) is a simple yet effective approach to compute similarity
between images. Similarity kernels at different regions and scales are usually fused by some …

In the statistical learning framework, the use of appropriate kernels may be the key for
substantial improvement in solving a given problem. In essence, a kernel is a similarity …