most citedExploiting Local Features from Deep Networks for Image Retrieval

85 citations · 86 across the 2 of their papers we have counts for

collaborators

8 papers

cs.CV2016

Weakly Supervised Learning of Heterogeneous Concepts in Videos

Sohil Shah, Kuldeep Kulkarni, Arijit Biswas +3

Typical textual descriptions that accompany online videos are 'weak': i.e., they mention the main concepts in the video but not their corresponding spatio-temporal locations. The c…

cs.CV2016

Mining Discriminative Triplets of Patches for Fine-Grained Classification

Yaming Wang, Jonghyun Choi, Vlad I. Morariu +1

Fine-grained classification involves distinguishing between similar sub-categories based on subtle differences in highly localized regions; therefore, accurate localization of disc…

cs.CV2016

Scalable Gaussian Processes for Supervised Hashing

Bahadir Ozdemir, Larry S. Davis

We propose a flexible procedure for large-scale image search by hash functions with kernels. Our method treats binary codes and pairwise semantic similarity as latent and observed…

cs.CV2016

Learning Temporal Regularity in Video Sequences

Mahmudul Hasan, Jonghyun Choi, Jan Neumann +2

Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach thi…

cs.CV2016

Supervised Incremental Hashing

Bahadir Ozdemir, Mahyar Najibi, Larry S. Davis

We propose an incremental strategy for learning hash functions with kernels for large-scale image search. Our method is based on a two-stage classification framework that treats bi…

cs.CV2016

Parameterizing Region Covariance: An Efficient Way To Apply Sparse Codes On Second Order Statistics

Xiyang Dai, Sameh Khamis, Yangmuzi Zhang +1

Sparse representations have been successfully applied to signal processing, computer vision and machine learning. Currently there is a trend to learn sparse models directly on stru…