476,178 research outputs found
Towards Effective Codebookless Model for Image Classification
The bag-of-features (BoF) model for image classification has been thoroughly
studied over the last decade. Different from the widely used BoF methods which
modeled images with a pre-trained codebook, the alternative codebook free image
modeling method, which we call Codebookless Model (CLM), attracted little
attention. In this paper, we present an effective CLM that represents an image
with a single Gaussian for classification. By embedding Gaussian manifold into
a vector space, we show that the simple incorporation of our CLM into a linear
classifier achieves very competitive accuracy compared with state-of-the-art
BoF methods (e.g., Fisher Vector). Since our CLM lies in a high dimensional
Riemannian manifold, we further propose a joint learning method of low-rank
transformation with support vector machine (SVM) classifier on the Gaussian
manifold, in order to reduce computational and storage cost. To study and
alleviate the side effect of background clutter on our CLM, we also present a
simple yet effective partial background removal method based on saliency
detection. Experiments are extensively conducted on eight widely used databases
to demonstrate the effectiveness and efficiency of our CLM method
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI). These dynamic images are constructed from a sequence of depth maps
using bidirectional rank pooling to effectively capture the spatial-temporal
information. Such image-based representations enable us to fine-tune the
existing ConvNets models trained on image data for classification of depth
sequences, without introducing large parameters to learn. Upon the proposed
representations, a convolutional Neural networks (ConvNets) based method is
developed for gesture recognition and evaluated on the Large-scale Isolated
Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The
method achieved 55.57\% classification accuracy and ranked place in
this challenge but was very close to the best performance even though we only
used depth data.Comment: arXiv admin note: text overlap with arXiv:1608.0633
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