Название: Semantic and Interactive Content-based Image RetrievalАвтор: Björn Barz
Издательство: Cuvillier
Год: 2020
Страниц: 323
Язык: английский
Формат: pdf (true)
Размер: 22.4 MB
Content-based image retrieval (CBIR) aims for finding images in large databases such as the internet based on their content. Given an exemplary query image provided by the user, the retrieval system provides a ranked list of similar images. Most contemporary CBIR systems compare images solely by means of their visual similarity, i.e., the occurrence of similar textures and the composition of colors. However, visual similarity does not necessarily coincide with semantic similarity. The predominant technique for end-to-end learning are artificial neural networks (ANNs), which consist of a stack of several layers that apply a non-linear transformation to the output of the previous layer. When dealing with images, this transformation typically consists in a set of learned convolutions followed by a non-linear element-wise function. While the earlier layers typically encode low-level image features such as edges, the degree of abstraction increases from layer to layer. For this reason, end-to-end learning using ANNs is widely known as Deep Learning.
