Adaptive graph formulation for 3D shape representation

3D shape recognition has attracted a great interest in computer vision due to its large number of important and exciting applications. This has led to exploring a variety of approaches to develop more efficient 3D analysis methods. However, current works take into account descriptions of global shape to generate models, ignoring small differences causing the problem of mismatching, especially for high similarity shapes. The present paper, therefore, proposes a new approach to represent 3D shapes based on graph formulation and its spectral analysis which can accurately represent local details and small surface variations. An adaptive graph is generated over the 3D shape to characterise the topology of the shape, followed by extracting a set of discriminating features to characterise the shape structure to train a classifier. The evaluation results show that the proposed method exceeds the state-of-the-art performance by 4% for a challenging dataset

AGSF: Adaptive graph formulation and hand-crafted graph spectral features for shape representation

Addressing intra-class variation in high similarity shapes is a challenging task in shape representation due to highly common local and global shape characteristics. Therefore, this paper proposes a new set of hand-crafted features for shape recognition by exploiting spectral features of the underlying graph adaptive connectivity formed by the shape characteristics. To achieve this, the paper proposes a new method for formulating an adaptively connected graph on the nodes of the shape outline. The adaptively connected graph is analysed in terms of its spectral bases followed by extracting hand-crafted adaptive graph spectral features (AGSF) to represent both global and local characteristics of the shape. Experimental evaluation using five 2D shape datasets and four challenging 3D shape datasets shows improvements with respect to the existing hand-crafted feature methods up to 9.14% for 2D shapes and up to 14.02% for 3D shapes. Also for 2D datasets, the proposed AGSF has outperformed the deep learning methods by 17.3%

Graph spectral domain shape representation

One of the major challenges in shape matching is recognising and interpreting the small variations in objects that are distinctly similar in their global structure, as in well known ETU10 silhouette dataset and the Tool dataset. The solution lies in modelling these variations with numerous precise details. This paper presents a novel approach based on fitting shape's local details into an adaptive spectral graph domain features. The proposed framework constructs an adaptive graph model on the boundaries of silhouette images based on threshold, in such a way that reveals small differences. This follows feature extraction on the spectral domain for shape representation. The proposed method shows that interpreting local details leading to improve the accuracy levels by 2% to 7% for the two datasets mentioned above, respectively

Graph spectral domain feature learning with application to in-air hand-drawn number and shape recognition

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

GHOSM : Graph-based Hybrid Outline and Skeleton Modelling for shape recognition

An efficient and accurate shape detection model plays a major role in many research areas.With the emergence of more complex shapes in real life applications, shape recognition models need to capture the structure with more effective features in order to achieve high accuracy rates for shape recognition. This paper presents a new method for 2D/3D shape recognition based on graph spectral domain handcrafted features, which are formulated by exploiting both an outline and a skeleton shape through the global outline and internal details. A fully connected graph is generated over the shape outline to capture the global outline representation while a hierarchically clustered graph with adaptive connectivity is formed on the skeleton to capture the structural descriptions of the shape. We demonstrate the ability of the Fiedler vector to provide the graph partitioning of the skeleton graph. The performance evaluation demonstrates the efficiency of the proposed method compared to state-of-the-art studies with increments of 4.09%, 2.2% and 14.02% for 2D static hand gestures, 2D shapes and 3D shapes, respectively