A hierarchical algorithm for image multi-labeling
Jiwei Hu, Kin‐Man Lam, Guoping Qiu · 2010
This paper presents an efficient two-stage method for multi-class image labeling. We first propose a simple label-filtering algorithm (LFA), which can remove most of the irrelevant labels for a query image while the potential labels are maintained. With a small population of potential labels left, we then apply the Naive-Bayes Nearest-Neighbor (NBNN) classifier as the second stage of our algorithm to identify the labels for the query image. This approach has been evaluated on the Corel database, and compared to existing algorithms. Experiment results show that our proposed algorithm can achieve a promising result, as it outperforms existing algorithms.