Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
This work addresses efficiency issues in image classification for computer vision applications, but it is incremental as it modifies an existing encoding scheme within the SPM framework.
The paper tackled the high computational cost of encoding local descriptors in Spatial Pyramid Matching (SPM) variants by proposing a Low Rank Representation (LRR)-based method called LrrSPM, which achieved competitive recognition rates on nine image datasets while being more efficient than existing methods like ScSPM.
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.