… However, it is impractical to use such kernels for large … match kernels (EMK) that map
local features to a low dimensional feature space and average the resulting vectors to form a set…

… In this work we present the pyramid match kernel—a new kernel function over unordered
feature sets that … We call the proposed matching kernel the pyramid match kernel because input …

… in the space of vector sets. In this work we propose a pyramid match kernel – a new kernel
function over unordered feature sets that allows them to be used effectively and efficiently in …

… match kernels into a global match kernel between image sets, which is learned in a supervised
way by kernel … A widely used match kernel between two sets of feature vectors is the sum …

… a dynamic match kernel by adaptively calculating the matching thresholds between query …
among deep CNN features. In contrast to the typical static match kernel which is independent …

… • We adapt the kernel descriptor framework of Bo [3] to … temporal match kernels by gathering
the formalism of explicit feature … to adapt the properties of the matching kernel and limits the …

… This section first describes the class of match kernels that we will … a set of images indexed
with ASMK\(^\star \), we cross-match … In practice, we cross match only features assigned to the …

… each image by a set of vectors (a bag-of-features), each feature representing a sub-part (a …
two images is then measured by matching the similarity between the descriptor sets [1], [2]. In …

… We observe that SIFT features in conjunction with efficient match kernels work well on this
dataset and obtain 70.8% accuracy using a single patch size, which beat SPM with the same …

… Since the matching process is asymmetric, our kernel is the average of the two matching …
nearby features in one image to match nearby features in the second one while discouraging …