Spectral clustering of ROIs for object discovery

Abstract

Object discovery is one of the most important applications of unsupervised learning. This paper addresses several spectral clustering techniques to attain a categorization of objects in images without additional information such as class labels or scene descriptions. Due to the fact that background textures bias the performance of image categorization methods, a generic object detector based on some general requirements on objects is applied. The object detector provides rectangular regions of interest (ROIs) as object hypotheses independent of the underlying object class. Feature extraction is simply constrained to these bounding boxes to decrease the influence of background clutter. Another aspect of this work is the utilization of a Gaussian mixture model (GMM) instead of k-means as usually used after feature transformation in spectral clustering. Several experiments have been done and the combination of spectral clustering techniques with the object detector is compared to the standard approach of computing features of the whole image. © 2011 Springer-Verlag.