Computer Science > Computer Vision and Pattern Recognition
[Submitted on 22 Sep 2014 (v1), last revised 2 Oct 2015 (this version, v2)]
Title:Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
View PDFAbstract: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.
Submission history
From: Xi Peng [view email][v1] Mon, 22 Sep 2014 13:35:34 UTC (2,899 KB)
[v2] Fri, 2 Oct 2015 14:12:58 UTC (7,864 KB)
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