Search Results for author:
Found 8 papers, 2 papers with code
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
However, training deep hashing networks for the task is challenging due to the binary constraints on the hash codes, the similarity preserving property, and the requirement for a vast amount of labelled images.
On-device Scalable Image-based Localization via Prioritized Cascade Search and Fast One-Many RANSAC
In order to overcome the resource constraints of mobile devices, we propose a system design that leverages the scalability advantage of image retrieval and accuracy of 3D model-based localization.
From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval
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 representations.
Compact Hash Code Learning with Binary Deep Neural Network
This design has overcome a challenging problem in some previous works: optimizing non-smooth objective functions because of binarization.
Supervised Hashing with End-to-End Binary Deep Neural Network
Image hashing is a popular technique applied to large scale content-based visual retrieval due to its compact and efficient binary codes.
Selective Deep Convolutional Features for Image Retrieval
Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors.
Enhance Feature Discrimination for Unsupervised Hashing
We introduce a novel approach to improve unsupervised hashing.
Simultaneous Feature Aggregating and Hashing for Large-scale Image Search
This feature vector is then subjected to a hashing function that produces a binary hash code.
