Abstract
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%.
Highlights
O BJECT recognition in terms of shape analysis has recently received a great attention in the field of computer vision [1] and applications, such as, security [2], medical imaging [3] and human activity and pose understanding [4]
This paper has proposed a new set of hand-crafted features (AGSF) for shape recognition by exploiting spectral features of the underlying graph adaptive connectivity formed by the shape characteristics
We have proposed a new method for formulating an adaptively connected graph to represent shapes with an unique graph structure
Summary
O BJECT recognition in terms of shape analysis has recently received a great attention in the field of computer vision [1] and applications, such as, security [2], medical imaging [3] and human activity and pose understanding [4]. The detection of shape appearance, part-structure, occlusion, articulation, and local details play an important role in the ways of shape classification Representation of these characteristics is significant when it comes to distinguishing highly similar shapes. The capturing small local details and prominent parts as well as the global structure into shape models is an important factor in distinguishing between different objects This becomes even more difficult for 3D shapes due to the complexity and different view-points of shapes. Inspired by the human vision literature, in this paper we propose a novel approach for shape representation by considering the shape as a connected graph, whose node connectivity is formulated adaptively, and analysing the spectral properties of the resulting graph. The proposed concept of adaptive formulation of connectivity, firstly computes a threshold to build a graph from shape nodes to capture complex shape structures and details.
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