Efficient layer-wise feature incremental approach for content-based image retrieval system

Sachendra Singh Chauhan; Shalini Batra

doi:10.1117/1.JEI.28.2.023038

30 April 2019 Efficient layer-wise feature incremental approach for content-based image retrieval system

Sachendra Singh Chauhan, Shalini Batra

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 2, 023038 (April 2019). https://doi.org/10.1117/1.JEI.28.2.023038

Abstract

Content-based image retrieval (CBIR) systems use multiple image features to represent an image. These systems suffer from curse of dimensionality since a high-dimensional feature vectors are formed to represent images in the target dataset. Reduction in length of feature vector can speed-up the retrieval process, but it reduces the retrieval accuracy. To overcome the aforementioned problem, an efficient layer-wise feature incremental approach for the CBIR system has been proposed. The proposed approach uses three primitive image features namely color, texture, and shape. The retrieval process is accomplished in three layers, at first a layer-complete dataset is searched but only single-feature space is used. Top 10% of images most similar to query image are retained in the second layer. The second layer uses two features for similarity computation, and only 50% of the most similar images are passed to the third layer. Finally, the third layer uses all three features to compute the similarity. Our aim is to reduce the search space at subsequent layers and use multiple features for a reduced dataset at the final layer. The proposed approach is evaluated on four publicly available image datasets. The retrieval results on the basis of precision, recall, and f-score show the performance improvement in comparison to state-of-the-art CBIR systems.

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Sachendra Singh Chauhan and Shalini Batra "Efficient layer-wise feature incremental approach for content-based image retrieval system," Journal of Electronic Imaging 28(2), 023038 (30 April 2019). https://doi.org/10.1117/1.JEI.28.2.023038

Received: 12 December 2018; Accepted: 11 April 2019; Published: 30 April 2019

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