In this paper, we consider the fundamental problem of finding a nearest set from a collection of sets, to a query set. This problem has obvious applications in ...
For the query set process, we adopt a learning-to-hash pipeline that turns the kernel representations into hash bits based on simple learners, using multi- ple ...
In this paper, we consider the fundamental problem of finding a nearest set from a collection of sets, to a query set. This problem has obvious applications in ...
This paper proposes a simple representation scheme that encodes both statistical and structural information of the sets, and adopts a learning-to-hash ...
Nov 2, 2017 · Experiments on two visual retrieval datasets show unambiguously that our set-to-set hashing framework outperforms prior methods that do not take ...
Missing: Supervised | Show results with:Supervised
At each step, a set of hash functions is selected by considering the hash functions' consistency or contribution in preserving the similari- ties of the input ...
Aug 30, 2017 · Supervised hashing methods have strong ability to overcome the semantic gap since they train objective function with the supervised information.
Supervised Hashing Models make use of semantic supervision, such as class labels or pairwise constraints (must-link and cannot-link), to guide the learning ...
In this paper, we propose a supervised hashing method for image retrieval, in which we automatically learn a good image representation tailored to hashing as ...
We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming ...