Image Classification by Feature Dimension Reduction and Graph based Ranking

TL;DR

This paper combines feature dimension reduction techniques with graph-based similarity learning to improve image classification accuracy and efficiency, demonstrating competitive results on an image database.

Contribution

It integrates NMF and deep subspace projection methods with graph-based similarity learning for enhanced image classification.

Findings

Improved classification accuracy over baseline methods

Effective reduction of feature dimensionality

Competitive performance on image database

Abstract

Dimensionality reduction (DR) of image features plays an important role in image retrieval and classification tasks. Recently, two types of methods have been proposed to improve the both the accuracy and efficiency for the dimensionality reduction problem. One uses Non-negative matrix factorization (NMF) to describe the image distribution on the space of base matrix. Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned. At the same time, the graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is also proposed for image class and retrieval problem. In this paper, after above two methods mentioned are utilized to reduce the high-dimensional features of images respectively, we learn the graph based similarity for the image classification problem. This paper compares the proposed approach with other approaches on an image database.