2,906 research outputs found
Unsupervised Network Pretraining via Encoding Human Design
Over the years, computer vision researchers have spent an immense amount of
effort on designing image features for the visual object recognition task. We
propose to incorporate this valuable experience to guide the task of training
deep neural networks. Our idea is to pretrain the network through the task of
replicating the process of hand-designed feature extraction. By learning to
replicate the process, the neural network integrates previous research
knowledge and learns to model visual objects in a way similar to the
hand-designed features. In the succeeding finetuning step, it further learns
object-specific representations from labeled data and this boosts its
classification power. We pretrain two convolutional neural networks where one
replicates the process of histogram of oriented gradients feature extraction,
and the other replicates the process of region covariance feature extraction.
After finetuning, we achieve substantially better performance than the baseline
methods.Comment: 9 pages, 11 figures, WACV 2016: IEEE Conference on Applications of
Computer Visio
Deep Poselets for Human Detection
We address the problem of detecting people in natural scenes using a part
approach based on poselets. We propose a bootstrapping method that allows us to
collect millions of weakly labeled examples for each poselet type. We use these
examples to train a Convolutional Neural Net to discriminate different poselet
types and separate them from the background class. We then use the trained CNN
as a way to represent poselet patches with a Pose Discriminative Feature (PDF)
vector -- a compact 256-dimensional feature vector that is effective at
discriminating pose from appearance. We train the poselet model on top of PDF
features and combine them with object-level CNNs for detection and bounding box
prediction. The resulting model leads to state-of-the-art performance for human
detection on the PASCAL datasets
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
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-level visual features and spatial pooling.
Incorporating spatial pooling improves the translational invariance and thus
the robustness of the detection process. We then directly optimise the partial
area under the ROC curve (\pAUC) measure, which concentrates detection
performance in the range of most practical importance. The combination of these
factors leads to a pedestrian detector which outperforms all competitors on all
of the standard benchmark datasets. We advance state-of-the-art results by
lowering the average miss rate from to on the INRIA benchmark,
to on the ETH benchmark, to on the TUD-Brussels
benchmark and to on the Caltech-USA benchmark.Comment: 16 pages. Appearing in Proc. European Conf. Computer Vision (ECCV)
201
Deep Boosting: Layered Feature Mining for General Image Classification
Constructing effective representations is a critical but challenging problem
in multimedia understanding. The traditional handcraft features often rely on
domain knowledge, limiting the performances of exiting methods. This paper
discusses a novel computational architecture for general image feature mining,
which assembles the primitive filters (i.e. Gabor wavelets) into compositional
features in a layer-wise manner. In each layer, we produce a number of base
classifiers (i.e. regression stumps) associated with the generated features,
and discover informative compositions by using the boosting algorithm. The
output compositional features of each layer are treated as the base components
to build up the next layer. Our framework is able to generate expressive image
representations while inducing very discriminate functions for image
classification. The experiments are conducted on several public datasets, and
we demonstrate superior performances over state-of-the-art approaches.Comment: 6 pages, 4 figures, ICME 201
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