Local image descriptors play a crucial role in many image processing tasks, such as object tracking, object recognition, panorama stitching, and image retrieval. In this paper, we focus on learning local image descriptors in an unsupervised way, …
With the ever-increasing availability of data, the need for efficient and accurate image retrieval methods has become larger and larger. Deep hashing has proven to be a promising solution, by defining a hash function to convert the data into a …
In this paper, we propose an efficient method for learning local image descriptor and its inversion function using a variational autoencoder (VAE). We design a loss function of the VAE specifically for this purpose, which, on one hand, incentivises …
In this paper, we present a novel approach for designing local image descriptors that learn from data and from hand-crafted descriptors. In particular, we construct a learning model that first mimics the behaviour of a hand-crafted descriptor and …
In this paper, we propose an efficient method for learning local image descriptors suitable for the use in image inpainting algorithms. We learn the descriptors using a convolutional autoencoder network that we design such that the network produces a …
In this paper, we propose an efficient method for learning local image descriptors with convolutional autoencoders. We design an autoencoder architecture that yields computationally efficient extraction of patch descriptors through an intermediate …