TUTCRIS - Tampereen teknillinen yliopisto


Local feature based unsupervised alignment of object class images



OtsikkoBMVC 2011 - Proceedings of the British Machine Vision Conference 2011
KustantajaBritish Machine Vision Association, BMVA
DOI - pysyväislinkit
TilaJulkaistu - 2011
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma2011 22nd British Machine Vision Conference, BMVC 2011 - Dundee, Iso-Britannia
Kesto: 29 elokuuta 20112 syyskuuta 2011


Conference2011 22nd British Machine Vision Conference, BMVC 2011


Alignment of objects is a predominant problem in part-based methods for visual object categorisation (VOC). These methods should learn the parts and their spatial variation, which is difficult for objects in arbitrary poses. A straightforward solution is to annotate images with a set of "object landmarks", but due to laborious manual annotation, semi-supervised methods requiring only a set of images and class labels are preferred. Recent state-of-the-art VOC methods utilise various approaches to align objects or otherwise compensate their geometric variation, but no explicit solution to the alignment problem with quantitative results can be found. The problem has been studied in the recent works related to "image congealing". The congealing methods, however, are based on image-based processing, and thus require moderate initial alignment and are sensitive to intra-class variation and background clutter. In this work, we define a local feature based algorithm to rigidly align object class images. Our algorithm is based on the standard VOC tools: local feature detectors and descriptors, correspondence based homography estimation, and random sample consensus (RANSAC) based spatial validation of local features. We first demonstrate how an intuitive feature matching approach works for simple classes, but fails for more complex ones. This is solved by a spatial scoring procedure which is the core element in the proposed method. Our method is compared to a state-of-the-art congealing method with realistic and difficult Caltech-101 and randomised Caltech-101 (r-Caltech-101) categories for which our method achieves clearly superior performance.

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