Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem

Abstract

Automatic Image Annotation is an important research topic in pattern recognition area. There are many different approaches to Automatic Image Annotation. In many of these approaches a key problem is determining correct word distributions in the generated annotations. Incorrect word distributions (word frequencies) results in large reduction of annotation quality. The paper presents a generic approach to find correct word frequencies. The approach may be used with many automatic image annotators, based on various machine learning paradigms. Proposed method is an improved version of our already presented method, called Greedy Resulted Word Counts Optimization. The key differences and novelties in the paper are: the concept of border support values and a method of optimal step selection in the optimization routine. Optimal step selection allows to reduce the number of computations during the optimization procedure, comparing to old Greedy Resulted Word Counts Optimization method.