International Conference on Advanced Computer Science and Information Systems · 2012 · 48 citations · 0 references
ComputingComputer ScienceComputer VisionFeature EngineeringMachine VisionEdge DetectionFeature DetectionFeature ExtractionImage AnalysisImage ClassificationPattern RecognitionAccuracyCanny Edge DetectorClassification AccuracyClassification Accuracy AssessmentGabor FeaturesGabor FilteringGray-level Co-occurrence MatricesImage Feature Extraction
Batik, as a cultural heritage from Indonesia, has a lot of motifs based on certain patterns. This paper discusses feature extraction methods for the recognition of batik motifs in digital images. In this study, the use of several feature extraction methods have been compared in terms of their performance with several scenarios for testing level accuracy. The methods include Gray Level Co-occurrence Matrices (GLCM), Canny Edge Detection, and Gabor filters. The experimental results show that the use of GLCM features has performed the best with a classification accuracy reaching 80%.