A New Approach to Enhance Learning Data Sets for Object Classification

In this paper, we propose a novel solution to the problem of insufficient training data in complex image classification tasks. The presented method efficiently recommends new learning samples with the use of contour-based descriptors and Scalable Vocabulary Tree (SVT) technique. The SVT is built from SIFT features and may be used for identification and localization of objects of interest in the analyzed query images. In conditions where the training data set is not large enough, the SVT's classification accuracy is satisfactory and comparable to that achieved by the dedicated CNN network. However, the SVT approach surpasses the classic convnet approaches due to its incremental learning capability. The classification certainty is further increased thanks to contour-based analysis using Point Distance Histogram (PDH) descriptor. At this stage, the PDH representation of the analyzed object is matched with the information stored in the pattern database. This database contains a set of 360 shape outlines for each object that are extracted from its 3D model. This leads to higher classification accuracy of the presented approach.