Object Retrieval in Images using SIFT and R-CNN

Image-based object retrieval has numerous applications in the field of machine vision to inquire from an appropriate image or video sequence for a given query object. The object retrieval task is conventionally carried out by a set of handcrafted algorithms, which provides image depictions in the fashion of visual characteristics. During the last decade, an extensive change has been practiced to describe visual content from handcrafted characteristics to the application of machine learning approaches and to the real layout of the image descriptors. The extensive movement is based on Convolutional Neural Networks (CNN) which is popularly known as Deep Learning. This proposed work deals with a combination of both conventional and machine learning approaches to retrieve an image object from a given dataset. This is done by a series of activities such as feature extraction and storage of training images, query image selection and feature extraction. Similarity matching between database and query image features. Final retrieval is based on the objectness score.