Simple Representation and Approximate Search of Feature Vectors for Large-Scale Object Recognition

This paper presents two methods of large-scale recognition of planar objects with a simple representation and approximate search of local feature vectors. A central problem of the use of local feature vectors is the burden of computation and memory for finding nearest neighbors. To solve this problem, the proposed methods embody the following: (1) a simple bit representation of feature vectors and hashing enable us to fast access with less memory, (2) approximate search with query perturbation allows us to find approximate nearest neighbors efficiently. From large-scale experiments using 10,000 objects in the database and 2,000 query images, it was found that only 10‐20% of correct nearest neighbors were enough for achieving recognition rate of 98.0%. The processing time for achieving this rate was 8.3 ms / query (excluding time for feature extraction). We have also tested the scalability of a proposed method using the database of 100,000 objects and obtained the result of 92.3% accuracy in 4.5 ms /query.