CONTENT BASED IMAGE RETRIEVAL METHOD WITH DISCRETE COSINE FEATURE EXTRACTION IN NATURAL IMAGES

Abstract

Data or information at this time is not only presented in written form, but also in the form of images that require greater storage. Most of the images in the digital world use the JPEG format, where the Discrete Cosine Transform is the heart of the JPEG format, the use of DCT coefficients for indexing and image retrieval causes the retrieving process to be slower because more coefficients are processed compared to the DC coefficient method, which is only 1 /64 (1 DC coefficient) of the DCT coefficient. In this research, we perform Content Based Image Retrieval with DC feature extraction of 15,000 natural images, then calculate the distance between the images using the Manhattan Distance method. The final result of calculating precision and recall shows a value of 0.6624 and a time of less than 2 seconds, with a maximum value of 1.876 seconds.