The document discusses a deep learning framework for image set classification that does not assume images lie on a certain geometric surface like existing methods. It introduces a Template Deep Reconstruction Model (TDRM) initialized through unsupervised pre-training with Gaussian Restricted Boltzmann Machines (GRBMs). Class-specific DRMs are then separately trained for each class and used to classify images based on minimum reconstruction error, outperforming existing state-of-the-art methods.

1. DEEP RECONSTRUCTION MODELS FOR IMAGE SET CLASSIFICATION
ABSTRACT
Image set classification finds its applications in a number of real-life scenarios such as
classification from surveillance videos, multi-view camera networks and personal albums.
Compared with single image based classification, it offers more promises and has therefore
attracted significant research attention in recent years. Unlike many existing methods which
assume images of a set to lie on a certain geometric surface, this paper introduces a deep learning
framework which makes no such prior assumptions and can automatically discover the
underlying geometric structure. Specifically, a Template Deep Reconstruction Model (TDRM) is
defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise
fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The initialized TDRM is then
separately trained for images of each class and class-specific DRMs are learnt. Based on the
minimum reconstruction errors from the learnt class-specific models, three different voting
strategies are devised for classification. Extensive experiments are performed to demonstrate the
efficacy of the proposed framework for the tasks of face and object recognition from image sets.
Experimental results show that the proposed method consistently outperforms the existing state
of the art methods.