278 research outputs found
Joint Regression and Ranking for Image Enhancement
Research on automated image enhancement has gained momentum in recent years,
partially due to the need for easy-to-use tools for enhancing pictures captured
by ubiquitous cameras on mobile devices. Many of the existing leading methods
employ machine-learning-based techniques, by which some enhancement parameters
for a given image are found by relating the image to the training images with
known enhancement parameters. While knowing the structure of the parameter
space can facilitate search for the optimal solution, none of the existing
methods has explicitly modeled and learned that structure. This paper presents
an end-to-end, novel joint regression and ranking approach to model the
interaction between desired enhancement parameters and images to be processed,
employing a Gaussian process (GP). GP allows searching for ideal parameters
using only the image features. The model naturally leads to a ranking technique
for comparing images in the induced feature space. Comparative evaluation using
the ground-truth based on the MIT-Adobe FiveK dataset plus subjective tests on
an additional data-set were used to demonstrate the effectiveness of the
proposed approach.Comment: WACV 201
Neural Dataset Generality
Often the filters learned by Convolutional Neural Networks (CNNs) from
different datasets appear similar. This is prominent in the first few layers.
This similarity of filters is being exploited for the purposes of transfer
learning and some studies have been made to analyse such transferability of
features. This is also being used as an initialization technique for different
tasks in the same dataset or for the same task in similar datasets.
Off-the-shelf CNN features have capitalized on this idea to promote their
networks as best transferable and most general and are used in a cavalier
manner in day-to-day computer vision tasks.
It is curious that while the filters learned by these CNNs are related to the
atomic structures of the images from which they are learnt, all datasets learn
similar looking low-level filters. With the understanding that a dataset that
contains many such atomic structures learn general filters and are therefore
useful to initialize other networks with, we propose a way to analyse and
quantify generality among datasets from their accuracies on transferred
filters. We applied this metric on several popular character recognition,
natural image and a medical image dataset, and arrived at some interesting
conclusions. On further experimentation we also discovered that particular
classes in a dataset themselves are more general than others.Comment: Long version of the paper accepted at IEEE International Conference
on Image Processing 201
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