2.7k citations · 7.3k across the 172 of their papers we have counts for
10 papers · 2 filters
Hashing on Nonlinear Manifolds
Fumin Shen, Chunhua Shen, Qinfeng Shi +3
Learning based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these meth…
The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classification
Lingqiao Liu, Chunhua Shen, Anton van den Hengel
A number of recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large dataset can be adopted as a universal image description which leads to a…
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors
Lingqiao Liu, Chunhua Shen, Lei Wang +2
Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Mo…
Mid-level Deep Pattern Mining
Yao Li, Lingqiao Liu, Chunhua Shen +1
Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective…
Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning
Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel
Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the basis of t…
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel
We propose a simple yet effective approach to the problem of pedestrian detection which outperforms the current state-of-the-art. Our new features are built on the basis of low-lev…
Fast Supervised Hashing with Decision Trees for High-Dimensional Data
Guosheng Lin, Chunhua Shen, Qinfeng Shi +2
Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have d…
Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
Peng Wang, Chunhua Shen, Anton van den Hengel +1
We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF inference problems. The core of our method is a very efficient bounding procedure, which combines scalable semi…
Deconstruction of compound objects from image sets
Anton van den Hengel, John Bastian, Anthony Dick +1
We propose a method to recover the structure of a compound object from multiple silhouettes. Structure is expressed as a collection of 3D primitives chosen from a pre-defined libra…
Large-margin Learning of Compact Binary Image Encodings
Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel
The use of high-dimensional features has become a normal practice in many computer vision applications. The large dimension of these features is a limiting factor upon the number o…