# On the Use of Deep Learning for Video Classification
## 3 Video Classification
### Geometric Deep Learning  
Shape descriptors play a significant role in the description of manifolds for 3D shapes. In general, a global feature descriptor is created by aggregating local descriptors to describe the geometric properties of the entire shape, for example, using the bag-of-features paradigm. A local feature descriptor assigns a vector to each point on the shape in a multi-dimensional descriptor space, representing the local structure of the shape around that point. Most deep learning techniques that deal with 3D shapes essentially use the CNN paradigm. Volumetric 2D multi-view shape representations are applied directly using standard (Euclidean) CNN architectures in neural networks via methods such as [96; 97]. These techniques are unsuited for dealing with deformable shapes because the shape descriptorsthey use are dependent on extrinsic structures that are invariant under Euclidean transformations, as demonstrated in Figure 8a [98], while some other approaches [99; 100; 101; 102; 103] create a new framework by adopting the CNN feature extraction pattern to investigate the inherent CNN versions that would enable handling shape deformations by using intrinsic filter structure, as shown in Figure 8b [98]. Geometric deep learning deals with non-Euclidean graph and manifold data. This type of data (irregularly arranged/distributed randomly) is usually used to describe geometric shapes. The purpose of geometric deep learning is to find the underlying patterns in geometric data where the traditional Euclidean distance-based deep learning approaches are not suitable. There are basically two methods available in the literature to apply deep learning on geometric data: (i) extrinsic methods and (ii) intrinsic methods. The filters in extrinsic methods are applied on the 3D surfaces such that it effects the structural deformity due to the extrinsic filter structure. The key weakness of extrinsic approaches [96; 97] is that they continue to consider geometric data as Euclidean information. When an object's position or shape changes, the extrinsic data representation fails. Additionally, for these methods to support the challenging-in-practice task of attaining the invariance of shape deformation, complicated models and extensive training are required. The filters in intrinsic approaches are applied on the 3D surfaces without being affected by the structural deformity. Rather than Euclidean realization, intrinsic methods work on the manifold and are isometry-invariant by construction. Some of the works based on intrinsic deep learning include (i) geodesic CNN [99], (ii) anisotropic CNN [100], (iii) mixture model network [101], (iv) structured prediction model [102], (v) localized spectral CNN [103], (vi) PointNet [104], (vii) PointNet++ [105], and (viii) RGA-MLP [106]. The application of geometric deep learning (mostly intrinsic methods) in analyzing videos can help in better understanding from the machine perspective, but it is still an open research problem and needs further investigation. For further details on geometric deep learning, readers are referred to [98; 107]. Figure 8: Illustration of deep learning approaches on geometric data. (**a**) Extrinsic method and (**b**) intrinsic method. \begin{table}
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**Study** & **Features** & **Model** & **Evaluation** & **Dataset** & **Problem** & **Fusion** & **Findings** \\ \hline \hline \multirow{8}{*}{} & Spatiotemporal feature & ARTNet by integrating & \multirow{8}{*}{Tpp-1} & \multirow{8}{*}{Multi-class} & \multirow{8}{*}{Concatenation and reduction operation. (3D convolution operation)} \\  & learning & the SMART block into the & & & & & & \\  & & & & &