What's that Style? A CNN-based Approach for Classification and Retrieval of Building Images

Image classification and content-based image retrieval (CBIR) are important problems in the field of computer vision. In recent years, convolutional neural networks (CNNs) have become the tool of choice for building state-of-the-art image classification systems. In this paper, we propose novel mid-l...

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Bibliographic Details
Published in2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) pp. 1 - 6
Main Authors Meltser, Rachel D., Banerji, Sugata, Sinha, Atreyee
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2017
Subjects
Buildings
Eigenvalues and eigenfunctions
Feature extraction
Principal component analysis
Support vector machines
Task analysis
Visualization
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DOI10.1109/ICAPR.2017.8593206

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Summary:Image classification and content-based image retrieval (CBIR) are important problems in the field of computer vision. In recent years, convolutional neural networks (CNNs) have become the tool of choice for building state-of-the-art image classification systems. In this paper, we propose novel mid-level representations involving the use of a pre-trained CNN for feature extraction and use them to solve both the classification and the retrieval problems on a dataset of building images with different architectural styles. We experimentally establish our intuitive understanding of the CNN features from different layers, and also combine the proposed representations with several different pre-processing and classification techniques to form a novel architectural image classification and retrieval system.
DOI:10.1109/ICAPR.2017.8593206