In this study, our primary aim is to determine empirically the role that skill plays in determining image aesthetics, and
whether it can be deciphered from the ratings given by a diverse group of judges. To this end, we have collected and
analyzed data from a large number of subjects (total 168) on a set of 221 of images taken by 33 photographers having
different photographic skill and experience. We also experimented with the rating scales used by previous studies in this
domain by introducing a binary rating system for collecting judges’ opinions. The study also demonstrates the use of
Amazon Mechanical Turk as a crowd-sourcing platform in collecting scientific data and evaluating the skill of the judges
participating in the experiment. We use a variety of performance and correlation metrics to evaluate the consistency of
ratings across different rating scales and compare our findings. A novel feature of our study is an attempt to define a
threshold based on the consistency of ratings when judges rate duplicate images. Our conclusion deviates from earlier
findings and our own expectations, with ratings not being able to determine skill levels of photographers to a statistically
significant level.
Matching shapes accurately is an important requirement in various applications; the most notable of which is object recognition. Precisely matching shapes is a difficult task and is an active area of research in the computer vision community. Most shape matching techniques rely on the contour of the object to provide the object's shape properties. However, we show that using the contour alone cannot help in matching all kinds of shapes. Many objects are recognised because of their overall visual similarity, rather than just their contour properties. In this paper, we assert that modelling the interior properties of the shape can help in extracting this overall visual similarity. We propose a simple way to extract the shape's interior properties. This is done by densely sampling points from within the shape and using it to describe the shape's features. We show that using such an approach provides an effective way to perform matching of shapes that are visually similar to each other, but have vastly different contour properties.
In this paper, we identify some of the existing problems in shape context matching. We first identify the need for reflection
invariance in shape context matching algorithms and propose a method to achieve the same. With the use of these reflection
invariance techniques, we bring all the objects, in a database, to their canonical form, which halves the time required to
match two shapes using their contexts. We then show how we can build better shape descriptors by the use of geodesic
information from the shapes and hence improve upon the well-known Inner Distance Shape Context (IDSC). The IDSC is
used by many pre- and post-processing algorithms as the baseline shape-matching algorithm. Our improvements to IDSC
will remain compatible for use with those algorithms. Finally, we introduce new comparison metrics that can be used for
the comparison of two or more algorithms. We have tested our proposals on the MPEG-7 database and show that our
methods significantly outperform the IDSC.
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