Matrix Factorization-Based Clustering Of Image Features For Bandwidth-Constrained Information Retrieval

TL;DR

This paper introduces a clustering method using PCA and NMF for image features to enable efficient, robust, and bandwidth-constrained image retrieval from mobile devices, achieving high accuracy.

Contribution

It proposes a novel clustering approach combining PCA and NMF for image features to improve retrieval accuracy and efficiency in bandwidth-limited scenarios.

Findings

Achieved 89% top-1 retrieval accuracy.

Achieved 92.5% top-3 retrieval accuracy.

Reduced computational complexity at the server.

Abstract

We consider the problem of accurately and efficiently querying a remote server to retrieve information about images captured by a mobile device. In addition to reduced transmission overhead and computational complexity, the retrieval protocol should be robust to variations in the image acquisition process, such as translation, rotation, scaling, and sensor-related differences. We propose to extract scale-invariant image features and then perform clustering to reduce the number of features needed for image matching. Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) are investigated as candidate clustering approaches. The image matching complexity at the database server is quadratic in the (small) number of clusters, not in the (very large) number of image features. We employ an image-dependent information content metric to approximate the model order, i.e., the number of clusters, needed for accurate matching, which is preferable to setting the model order using trial and error. We show how to combine the hypotheses provided by PCA and NMF factor loadings, thereby obtaining more accurate retrieval than using either approach alone. In experiments on a database of urban images, we obtain a top-1 retrieval accuracy of 89% and a top-3 accuracy of 92.5%.