Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification

TL;DR

This paper introduces LrrSPM, a novel low-rank representation method within Spatial Pyramid Matching that improves efficiency and maintains competitive accuracy in image classification tasks.

Contribution

It proposes using Low Rank Representation (LRR) for encoding descriptors in SPM, offering a more efficient alternative to existing methods like ScSPM.

Findings

LrrSPM outperforms ScSPM in efficiency.

LrrSPM achieves comparable recognition rates.

The method is validated on nine image datasets.

Abstract

Spatial Pyramid Matching (SPM) and its variants have achieved a lot of success in image classification. The main difference among them is their encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of Vector Quantization (VQ) into the framework of SPM. Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image. In this paper, we propose using Low Rank Representation (LRR) to encode the descriptors under the framework of SPM. Different from SC, LRR considers the group effect among data points instead of sparsity. Benefiting from this property, the proposed method (i.e., LrrSPM) can offer a better performance. To further improve the generalizability and robustness, we reformulate the rank-minimization problem as a truncated projection problem. Extensive experimental studies show that LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving competitive recognition rates on nine image data sets.