Wood identification based on histogram of oriented gradient (HOG) feature and support vector machine (SVM) classifier

Forest areas in Indonesia covered about 2/3 of total land areas which has about 4000 wood species. Wood identification plays a key role in wood utilization not only for determining appropriate use but also for supporting legal timber trade. However, the identification process requires high expertise and complex method which can be done in the laboratory. In order to simplify the identification process, we develop wood identification using computer vision by using Histogram of Oriented Gradient (HOG) to extract the species of wood and Support Vector Machines (SVM) to classify wood species. These methods combination will improve the accuracy of wood identification process. The result showed that the HOG method can extract the texture of woods and SVM classifier can generate the boundary decision after executing the training process. By doing the testing process of SVM classifier, the result showed that the accuracy from the identification is 70.5% for using positive testing image and 77.5% for using negative testing image. This accuracy value can be reached because the texture for each training image has different texture pattern especially the number and location of vessels.