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ThinkMind // SIGNAL 2019, The Fourth International Conference on Advances in Signal, Image and Video Processing // View article signal_2019_1_20_60048


EFM-HOG: Improving Image Retrieval in the Wild

Authors:
Sugata Banerji
Atreyee Sinha

Keywords: Computer Vision; Principal Component Analysis; Fisher Linear Discriminant; Enhanced Fisher Model; Histogram of Oriented Gradients; Image Search

Abstract:
The problem of retrieving images from a dataset, which are similar to a query image is an important high-level vision problem. Different tasks define similarity based on different low-level features like shape, color or texture. In the presented work, we focus on the problem of retrieval of images of similarly shaped objects, with the query being an object selected from a query image at runtime. Towards this end, we propose a novel shape representation and associated similarity measure, which exploits the dimensionality reduction and feature extraction methods of Principal Component Analysis (PCA) and Enhanced Fisher Model (EFM). The effectiveness of this representation is demonstrated on large-scale image datasets for the task of object retrieval and the performance is compared to Histograms of Oriented Gradients (HOG).

Pages: 6 to 11

Copyright: Copyright (c) IARIA, 2019

Publication date: June 2, 2019

Published in: conference

ISSN: 2519-8432

ISBN: 978-1-61208-716-0

Location: Athens, Greece

Dates: from June 2, 2019 to June 6, 2019

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