Signature Similarity Search Using Cluster Image Retrieval - Pandapotan Siagian (ICON-CSE, 2014)
Significant image search in the database of signatures on previous research is still 65%, because the data stored that result capture camera [13, 14, ]. In this paper, we do the retrieval pattern signatures automatically, the algorithm that is used to find data points based on contributions. Similarity search algorithms will optimize the intra cluster and claster signatures in 3 classes i.e., Gx, Gy, gt. image capture-based content, which can calculate the similarity between the shape and texture of the images and do a grouping of pictures with minimum Euclidean distance consideration. CBIR is a set of techniques for taking pictures of semantically relevant than just image database based on recommended sources of images automatically. Performance evaluation method is now done with precision and recall for a different database. Response time to find most of the signatures from 3 grade database, giving the effect of a 78% accuracy rate higher.