Class-constraint similarity queries

Abstract

Similarity searching is a widely applied concept on multimedia or complex data, such as images, videos, time-series, among others. Therefore, it is important to look at the execution of specific query types, e.g., constrained k-nearest neighbor that is directly based on bounded regions. In this paper, we present the Class-Constraint k-Nearest Neighbor (CCkNN) query, which goes beyond the traditional constrained k-nearest neighbor, because our CCkNN works for any specific categories of data points. The proposed CCkNN aims at accelerating the process of class-constraint similarity query execution by taking advantage of performing queries on multiple metric access methods regarding the class dimensions of the objects of each index. Additionally, this strategy identifies which index is more appropriate to run class-constraint on the k-nearest neighbor queries. Experimental results based on several datasets, including synthetic and real ones, show that our strategy can reduce the number of distance calculations in up to two orders of magnitude while keeping a high-quality retrieval, according to the classes of the objects queried.