Co-author(s) :

Sarukhanyan Hakob ; Саруханян Акоп

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.
; В данной работе представлено исследование системы поиска визуально похожих изображений, основанной на свойствах высшего уровня конволюционных нейронных сетей. Показано, что выбор функции расстояния для свойств высшего уровня Соод1еЫе1 имеет большое влияние на точность поисковой системы. Для получения лучших результатов из просмотренных функций выделяется функция корреляции.

Publisher:

Mathematical Problems of Computer Science

Identifier:

oai:noad.sci.am:136047

ISSN:

0131-4645

Language:

English

Volume:

45

URL:

click here to follow the link

Additional Information:

agasy18@gmail.com ; hakop@ipia.sci.am

Affiliation:

Institute for Informatics and Automation Problems of NAS RA

Country:

Armenia

Year:

2016

Indexing:

ASCI