Детайли за публикацията
(Publication details)

Autors: Popova, A. A., Neshov N. N.
Title: Combining Features Evaluation Approach in Content-Based Image Search for Medical Applications
Keywords: CBIR, image similarity search, feature selection, query by example, visual features, medical images

Abstract: In this paper we propose an approach for a feature combination helping to distinguish searched images from databases by retrieving relevant images. The retrieval effectiveness of 11 well known image features, commonly used in Content Based Image Retrieval (CBIR) systems, is investigated. We suggest a combined features approach including features’ performance comparison of 57 various medical image categories from IRMA Database. The most informative 3 features, adaptive to image categories, are defined. Based on experiments and image similarity accuracy analysis we suggest a set of 3 low level features Color Layout, Edge Histogram and DCT Coefficients. The developed approach achieves better similar images retrieval results for more image classes. The results show an accuracy improvement of 14.49% on Mean Average Precision (MAP). The comparison is done to the same type performance measure of the best individual feature in different medical image categories.

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Issue
, 2012, Germany, Springer-Verlag

Copyright Springer-Verlag

Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание

Въведена от: доц. д-р Антоанета Александрова Попова