Swarm-based Descriptor Combination and its Application for Image Classification

Abstract

In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model, a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach.

Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification

“…Even though the OPF classifier works in almost any application with its standard distance measure 1 , there might be the need to adjust it whenever it is incapable of producing an appropriate result. To the best of the authors' knowledge, few works attempt to incorporate multiple distance spaces in OPF classification [18,19], yet it does not provide a thorough comparison amongst various distance measures. Additionally, only a similar work attempts to verify the effects of distance measures in supervised learning; however, in K-Nearest Neighbor classification [20].…”