Performance analysis of combined descriptors for similar crop disease image retrieval

Abstract

In this paper, combined image descriptors that can improve the performance of similar crop disease image retrieval system are suggested. When combining descriptors, the similarity between images is calculated using a single descriptor first. And, new similarity which corresponds to the combined descriptors is created by calculating the sum of image similarity corresponding to descriptors to be combined. Lastly, the image retrieval is carried out based on the distance value corresponding to the combined descriptors. The experiment was carried out with a total of 742 images of 3 crops including pear, grape and strawberry using the combined descriptors. As the experimental result, we discovered that using combined descriptors improved the system performance generally. And, we proved that a proper combination of descriptors varied for each crop and we found such combination. We also discovered that a combination of descriptors producing a high F-measure value of the system was different from a combination of descriptors having a higher probability that more accurate retrieval results would be outputted in the beginning of the screen. Therefore, proper combined descriptors should be selected according to actual system requirements.