Comparison of Feature Selection Method with ReliefF base Multi Label Algorithm to Improve Semantic Image Classification
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Abstract
Image classification is developed as part of a framework in digital image processing. The image extraction in relevant features is the most challenging part of classification. The performance of algorithm depends on features considered from the dataset. There are many algorithms attempt to analyze and combine all features but high dimensional dataset degrades the performance of algorithm. To overcome this problem, we propose a ReliefF base on Multi Label Algorithm (ReliefF-ML) to improve semantic image classification. Feature selection technique is used as a first step to analyze in large data set that is to find relevant features and removes redundant features from high dimensional dataset. This paper presents four steps including (1) data preprocessing, (2) feature extraction, (3) ReliefF-ML feature selection model, and (4) efficiency measurement and evaluation of experimental results. The experimental results indicate that our framework offers performance improvements with ReliefF-ML feature selection algorithm. The proposed model can achieve significant improvements for image classification with maximum success rate of 78.87% with 12 features.
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