Attribute discovery via predictable discriminative binary codes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Measuring image distances via embedding in a semantic manifold
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
VISOR: towards on-the-fly large-scale object category retrieval
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Image retrieval using eigen queries
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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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.