7 citations · 16 across the 5 of their papers we have counts for
5 papers
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan +4
Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently. However, training deep hashing networks for the task…
Simultaneous Compression and Quantization: A Joint Approach for Efficient Unsupervised Hashing
Tuan Hoang, Thanh-Toan Do, Huu Le +2
For unsupervised data-dependent hashing, the two most important requirements are to preserve similarity in the low-dimensional feature space and to minimize the binary quantization…
From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval
Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan +3
In the large-scale image retrieval task, the two most important requirements are the discriminability of image representations and the efficiency in computation and storage of repr…
Selective Deep Convolutional Features for Image Retrieval
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan +1
Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to f…
Enhance Feature Discrimination for Unsupervised Hashing
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan +1
We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed met…