Talks and Poster Presentations (with Proceedings-Entry):
M. Hörhan, H. Eidenberger:
"The Gestalt Interest Points Distance Feature for Compact and Accurate Image Description";
Talk: IEEE International Symposium on Signal Processing and Information Technology,
- 2017-12-20; in: "Proceedings IEEE International Symposium on Signal Processing and Information Technology",
In this work, we present the novel Inter-GIP Distances (IGD) feature and
its integration into the Gestalt Interest Points (GIP) image descriptor.
With the ongoing growth of visual data, efficient image descriptor methods
are becoming more and more important. Several local point-based description
methods have been defined in the past decades. Accuracy and descriptor size
are important factors when selecting the appropriate method for a given
retrieval problem. The method presented in this work describes images with
only a few very compact descriptors. To test our descriptor, we developed
an image classification prototype and conducted several experiments with a
publicly available horses dataset and a food dataset. Our experiments show
that only a few of the very compact GIP image descriptors are necessary to
quickly classify the images from the datasets with high accuracy.
Furthermore, we compared our experimental results to state-of-the-art local
point-based description methods and found that our method is highly
content-based image analysis; local interest point detection; gestalt psychology; image processing; media understanding
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.