Abstract
This paper addresses the problem of recognition of dynamic shapes by representing the structure in a shape as a graph and learning the graph spectral domain features. Our proposed method includes pre-processing for converting the dynamic shapes into a fully connected graph, followed by analysis of the eigenvectors of the normalized Laplacian of the graph adjacency matrix for forming the feature vectors. The method proposes to use the eigenvector corresponding to the lowest eigenvalue for formulating the feature vectors as it captures the details of the structure of the graph. The use of the proposed graph spectral domain representation has been demonstrated in an in-air hand-drawn number and symbol recognition applications. It has achieved average accuracy rates of 99.56% and 99.44%, for numbers and symbols, respectively, outperforming the existing methods for all datasets used. It also has the added benefits of fast real-time operation and invariance to rotation and flipping, making the recognition system robust to different writing and drawing variations.
Highlights
Graph Signal Processing (GSP) [1]–[3] has attracted great attention in processing, analysis, coding and understanding of data sampled on a non-uniform grid, often referred to as irregular data or graph data
These advances coupled with emerging graph signal processing concepts has led us to propose a novel graph spectral domain representation for shapes as a solution to overcome these issues in dynamic shape recognition in this paper
The Quadratic Discriminant Analysis (QDA) function, and the Linear Discriminant Analysis (LDA) function are similar in terms of the function and classification rules except the way covariance matrix is computed separately for each class
Summary
Graph Signal Processing (GSP) [1]–[3] has attracted great attention in processing, analysis, coding and understanding of data sampled on a non-uniform grid, often referred to as irregular data or graph data. Another study on human vision suggests that the visual cortex perceives and understands shapes by representing the shape boundary as a connected set of nodes [24] These advances coupled with emerging graph signal processing concepts has led us to propose a novel graph spectral domain representation for shapes as a solution to overcome these issues in dynamic shape recognition in this paper. In converting the hand movement paths of the in-air drawn shapes into fully connected graphs, we aim to minimise the number of nodes while keeping the properties of the structure intact This leads to lowering the complexity without affecting the recognition accuracy rates.
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