Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data

TL;DR

This paper introduces a data augmentation strategy using a convolutional autoencoder to enhance clustering performance on crowdsourced heritage data, addressing data sparsity issues in deep clustering methods.

Contribution

It presents a novel approach of employing data augmentation with a CAE to improve deep clustering results on heritage datasets, which is a new application of augmentation in this context.

Findings

Improved clustering accuracy on Indian Heritage dataset

Consistent performance gains over existing methods

Enhanced cluster density through data augmentation

Abstract

In this paper, we propose a strategy to mitigate the problem of inefficient clustering performance by introducing data augmentation as an auxiliary plug-in. Classical clustering techniques such as K-means, Gaussian mixture model and spectral clustering are central to many data-driven applications. However, recently unsupervised simultaneous feature learning and clustering using neural networks also known as Deep Embedded Clustering (DEC) has gained prominence. Pioneering works on deep feature clustering focus on defining relevant clustering loss function and choosing the right neural network for extracting features. A central problem in all these cases is data sparsity accompanied by high intra-class and low inter-class variance, which subsequently leads to poor clustering performance and erroneous candidate assignments. Towards this, we employ data augmentation techniques to improve the density of the clusters, thus improving the overall performance. We train a variant of Convolutional Autoencoder (CAE) with augmented data to construct the initial feature space as a novel model for deep clustering. We demonstrate the results of proposed strategy on crowdsourced Indian Heritage dataset. Extensive experiments show consistent improvements over existing works.