Image Visual Similarity Based on High Level Features of Convolutional Neural Networks
Year: 2016
Type: article
Abstract: Nowadays, the task of similar content retrieval is one of the central topics of interest in academic and industrial worlds. There are numerous techniques that are both dealing good with structured data and unstructured such as texts, respectively. However, in this paper we present a technique for retrieval of similar image content. We embed images to N dimensional feature space using convolutional neural networks and perform the nearest neighbor search afterwards. At the end, several distance metrics and their influence on the outcome are discussed. We are rather interested in the proportion of related content than in the additional ranking. Thus, the evaluation of results is based on precision and recall. We have selected 6 major categories from ImageNet dataset to assess the performance.
Authors: Aghasi Poghosyan, Hakob Sarukhanyan
Institution: Institute for Informatics and Automation Problems
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2894825284