67,889 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
Understanding deep features with computer-generated imagery
We introduce an approach for analyzing the variation of features generated by
convolutional neural networks (CNNs) with respect to scene factors that occur
in natural images. Such factors may include object style, 3D viewpoint, color,
and scene lighting configuration. Our approach analyzes CNN feature responses
corresponding to different scene factors by controlling for them via rendering
using a large database of 3D CAD models. The rendered images are presented to a
trained CNN and responses for different layers are studied with respect to the
input scene factors. We perform a decomposition of the responses based on
knowledge of the input scene factors and analyze the resulting components. In
particular, we quantify their relative importance in the CNN responses and
visualize them using principal component analysis. We show qualitative and
quantitative results of our study on three CNNs trained on large image
datasets: AlexNet, Places, and Oxford VGG. We observe important differences
across the networks and CNN layers for different scene factors and object
categories. Finally, we demonstrate that our analysis based on
computer-generated imagery translates to the network representation of natural
images
- âŠ