Name
Abstract | ||
|---|---|---|
This research focuses on the problem of shape analysis on the 2D image domain. State of the art methods like convolution neural networks typically rely on the richness of texture information in images for inference. However, when the underlying object is better described as a shape, these methods tend to suffer. Here, we aim to bridge this gap by proposing a method to analyze shapes on images. The driving idea is to learn features defined on just a few point samples extracted from image super-pixels. A dynamic graph CNN (i.e., a neural net producing a different graph at each layer) is trained and used as the learning engine in a classification task. Our first set of experiments is tested on the 10-digit class MNIST dataset benchmark where we find that the proposed method performs better than others on small datasets. This altogether shows a promising direction for the analysis of more complex shapes fundamental in classification and retrieval of documents of scientific nature. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SSIAI49293.2020.9094594 | 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) |
Keywords | DocType | ISSN |
shape analysis,2D image domain,convolution neural networks,texture information,image super-pixels,dynamic graph CNN,learning engine,10-digit class MNIST dataset benchmark,learning shapes,image sampled points,document retrieval,document classification,scientific nature | Conference | 1550-5782 |
ISBN | Citations | PageRank |
978-1-7281-5746-7 | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Castorena | 1 | 0 | 0.34 |
| Diane Oyen | 2 | 0 | 0.68 |