Linear dimensionality reduction applied to SIFT and SURF feature descriptors
Ricardo Eugenio González Valenzuela · 2014
Robust local descriptors usually consist of high dimensional feature vectors to describe distinctive characteristics of images. The high dimensionality of a feature vector incurs into considerable costs in terms of computational time and storage requirements, which affects the performance of several tasks that employ feature vectors, such as matching, image retrieval, and classification. To address these problems, it is possible to apply some dimensionality reduction techniques by building a projection matrix, which explains adequately the importance of the data in other basis. This dissertation aims at applying linear dimensionality reduction to SIFT and SURF descriptors. Its main objective is to demonstrate that, even risking to decrease the accuracy of the feature vectors, the dimensionality reduction can result in a satisfactory trade-off between computational time and storage. We perform the linear dimensionality reduction through Random Projections (RP), Independent Component Analysis (ICA), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Partial Least Squares (PLS) in order to create lower dimensional feature vectors. This work evaluates such reduced feature vectors in a matching application, as well as their distinctiveness in an image retrieval application. The computational time and memory usage are then measured by comparing the original and the reduced feature vectors.