A Learning State-Space Model for Image Retrieval

A Learning State-Space Model for Image Retrieval

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This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval.We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation.The design of the concept units aims at Text Clustering Algorithm Based on Random Cluster Core describing similar characteristics at a certain perspective among relevant images.We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of Antibacterial activity of selenium nanoparticles/copper oxide (SeNPs/CuO) nanocomposite against some multi-drug resistant clinical pathogens our proposed model.This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.