원문정보
초록
영어
Flower classification is a useful way for grouping a flower in certain class using specific features. This research propose a new method of flower classification system using combination of color and texture features. The first phase is getting the crown of the flower, which is localized from a flower image by using pillbox filtering and OTSU’s thresholding. In the next phase, color and texture features are extracted from the crown. The color features are extracted by removing L channel in L*a*b* color space, and taking only a* and b* channel, because of ignoring different lighting condition in flower image. The texture features are extracted by Segmentation-based Fractal Texture Analysis (SFTA). The combination features which are consisted of 10 color features and 48 texture features are used as input in k-Nearest Neighbor (kNN) classifier method with cosine distance. The flower classification achieves the best result with accuracy 73.63%.
목차
1. Introduction
2. Research Method
2.1 Preprocessing
2.2. Feature Extraction
2.3. Flower Classification
3. Results and Analysis
3.1. Perfomance Measure
3.2. Results
4. Conclusion and Future Work
References