A Deep Learning Approach for Content-based Image Retrieval using Sparse Auto-Encoder (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2022)
Most search engines retrieve photographs using classic text-based algorithms that depend ondescriptions and metadata. Content-based image retrieval (CBIR), image categorization, andinvestigation have all gotten a lot of attention in the previous two decades. High-level picture viewsare represented as feature vectors consisting of numerical values in CBIR and image classificationbasedalgorithms.To isolate the main object from a picture, we first use segmentation and main objectdetection. The autoencoder is then used to extract features from the object and choose relevantfeatures.Various deep learning representations are trained and tested, and the outcomes are comparedto see which architecture maximises prediction scores while reducing computing costs in flowercategorization identification. Images of flowers will be used to train and test models, with a selectionof them being utilised for validation. Finally, the results of the experiments suggest that our techniquecan be used to search for images in a genuine picture database.