ExSDM: Novel Content-based Image Retrieval based on Sparse Distributed Memory Model

Abstract

In recent times, image retrieval has garnered an increasing amount of interest due to the introduction of image datasets of significant size. Many methods have been suggested to retrieve images swiftly and accurately. However, the majority of these techniques are centered on the representation of the image. It is felt that alongside the representation of the image, smart storage is required that can rise to the demands of the task. A possible solution is to model human visual memory, retrieving images by imitating the brain's detection processes. This paper proposes a memory model that can be employed as smart memory for efficiently retrieving images based on image hashes. The memory model accepts hash code inputs derived from DWT and DCT transformations. The model is evaluated in terms of the memory capacity and the accuracy of the image retrieval. The results demonstrate that this model has a greater capacity and is significantly quicker than other types of memory models.