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The complex image matching procedure on the large image data volume require a lot of processing power and storage space, which makes the cloud platforms very much adaptable for such CBIR systems. The cloud computing enables the capability of the CBIRs to expand the computing and storage resources as the requirement rises above the registered scale. The greatest challenge rises in the CBIRs when it comes to search the large volumes of encrypted image data. The image data is stored in the encrypted to protect the privacy of the data and to protect it from various information stealing attacks on the web storage resources. The proposed model has been designed in the layered approach, where the image features are matched one after one to prepare the final image ranking. The semantic memory has been also utilized in the form of semantic memory with fuzzy sets and user feedback. The semantic feature has been added to provide the capability to permanently filter the images marked by the users for the specific queries. The proposed model performance has been evaluated in the form of various performance parameters of accuracy, ranking/index building time, encryption/decryption times, semantic library lookup time, precision, recall, etc. The experimental results have proved the efficiency and robustness of the proposed model and proved it as the effective CBIR system.

Multi-Feature Ensemble with Support Vector Machine Classification for CBIR Models