Shih-Fu Chang

TL;DR: In this paper, results from searches for the standard model Higgs boson in proton-proton collisions at 7 and 8 TeV in the CMS experiment at the LHC, using data samples corresponding to integrated luminosities of up to 5.8 standard deviations.

TL;DR: The VisualSEEk system is novel in that the user forms the queries by diagramming spatial arrangements of color regions by utilizing color information, region sizes and absolute and relative spatial locations.

TL;DR: A novel kernel-based supervised hashing model which requires a limited amount of supervised information, i.e., similar and dissimilar data pairs, and a feasible training cost in achieving high quality hashing, and significantly outperforms the state-of-the-arts in searching both metric distance neighbors and semantically similar neighbors is proposed.

TL;DR: This paper proposes a novel graph-based hashing method which automatically discovers the neighborhood structure inherent in the data to learn appropriate compact codes and describes a hierarchical threshold learning procedure in which each eigenfunction yields multiple bits, leading to higher search accuracy.

TL;DR: This work proposes a semi-supervised hashing (SSH) framework that minimizes empirical error over the labeled set and an information theoretic regularizer over both labeled and unlabeled sets and presents three different semi- supervised hashing methods, including orthogonal hashing, nonorthogonal hash, and sequential hashing.