2,047 research outputs found
Object Level Deep Feature Pooling for Compact Image Representation
Convolutional Neural Network (CNN) features have been successfully employed
in recent works as an image descriptor for various vision tasks. But the
inability of the deep CNN features to exhibit invariance to geometric
transformations and object compositions poses a great challenge for image
search. In this work, we demonstrate the effectiveness of the objectness prior
over the deep CNN features of image regions for obtaining an invariant image
representation. The proposed approach represents the image as a vector of
pooled CNN features describing the underlying objects. This representation
provides robustness to spatial layout of the objects in the scene and achieves
invariance to general geometric transformations, such as translation, rotation
and scaling. The proposed approach also leads to a compact representation of
the scene, making each image occupy a smaller memory footprint. Experiments
show that the proposed representation achieves state of the art retrieval
results on a set of challenging benchmark image datasets, while maintaining a
compact representation.Comment: Deep Vision 201
Compensating for Large In-Plane Rotations in Natural Images
Rotation invariance has been studied in the computer vision community
primarily in the context of small in-plane rotations. This is usually achieved
by building invariant image features. However, the problem of achieving
invariance for large rotation angles remains largely unexplored. In this work,
we tackle this problem by directly compensating for large rotations, as opposed
to building invariant features. This is inspired by the neuro-scientific
concept of mental rotation, which humans use to compare pairs of rotated
objects. Our contributions here are three-fold. First, we train a Convolutional
Neural Network (CNN) to detect image rotations. We find that generic CNN
architectures are not suitable for this purpose. To this end, we introduce a
convolutional template layer, which learns representations for canonical
'unrotated' images. Second, we use Bayesian Optimization to quickly sift
through a large number of candidate images to find the canonical 'unrotated'
image. Third, we use this method to achieve robustness to large angles in an
image retrieval scenario. Our method is task-agnostic, and can be used as a
pre-processing step in any computer vision system.Comment: Accepted at Indian Conference on Computer Vision, Graphics and Image
Processing (ICVGIP) 201
LIFT: Learned Invariant Feature Transform
We introduce a novel Deep Network architecture that implements the full
feature point handling pipeline, that is, detection, orientation estimation,
and feature description. While previous works have successfully tackled each
one of these problems individually, we show how to learn to do all three in a
unified manner while preserving end-to-end differentiability. We then
demonstrate that our Deep pipeline outperforms state-of-the-art methods on a
number of benchmark datasets, without the need of retraining.Comment: Accepted to ECCV 2016 (spotlight
- …