Scalable object-class retrieval with approximate and top-k ranking


In this paper we address the problem of object-class retrieval in large image data sets: given a small set of training examples defining a visual category, the objective is to efficiently retrieve images of the same class from a large database. We propose two contrasting retrieval schemes achieving good accuracy and high efficiency. The first exploits sparse classification models expressed as linear combinations of a small number of features. These sparse models can be efficiently evaluated using inverted file indexing. Furthermore, we introduce a novel ranking procedure that provides a significant speedup over inverted file indexing when the goal is restricted to finding the top-k (i.e., the k highest ranked) images in the data set. We contrast these sparse retrieval models with a second scheme based on approximate ranking using vector quantization. Experimental results show that our algorithms for object-class retrieval can search a 10 million database in just a couple of seconds and produce categorization accuracy comparable to the best known class-recognition systems.

DOI: 10.1109/ICCV.2011.6126556

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@article{Rastegari2011ScalableOR, title={Scalable object-class retrieval with approximate and top-k ranking}, author={Mohammad Rastegari and Chen Fang and Lorenzo Torresani}, journal={2011 International Conference on Computer Vision}, year={2011}, pages={2659-2666} }