Document Type
Technical Report
Publication Date
7-5-2011
Technical Report Number
TR2011-700
Abstract
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.
Dartmouth Digital Commons Citation
Rastegari, Mohammad; Fang, Chen; and Torresani, Lorenzo, "Scalable Object-Class Search via Sparse Retrieval Models and Approximate Ranking" (2011). Computer Science Technical Report TR2011-700. https://digitalcommons.dartmouth.edu/cs_tr/343
