A Deep Learning Approach for Content-based Image Retrieval using Sparse Auto-Encoder

Abstract

Most search engines retrieve photographs using classic text-based algorithms that depend on
descriptions and metadata. Content-based image retrieval (CBIR), image categorization, and
investigation have all gotten a lot of attention in the previous two decades. High-level picture views
are represented as feature vectors consisting of numerical values in CBIR and image classificationbased
algorithms.To isolate the main object from a picture, we first use segmentation and main object
detection. The autoencoder is then used to extract features from the object and choose relevant
features.Various deep learning representations are trained and tested, and the outcomes are compared
to see which architecture maximises prediction scores while reducing computing costs in flower
categorization identification. Images of flowers will be used to train and test models, with a selection
of them being utilised for validation. Finally, the results of the experiments suggest that our technique
can be used to search for images in a genuine picture database.