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DCT-Inspired Feature Transform for Image Retrieval and Reconstruction

Research output: Contribution to journalArticlepeer-review

Yunhe Wang, Miaojing Shi, Shan You, Chao Xu

Original languageEnglish
Pages (from-to)4406-4420
Number of pages15
Issue number9
Early online date12 Jul 2016
Accepted/In press12 Jul 2016
E-pub ahead of print12 Jul 2016
PublishedSep 2016


  • DCT Inspired Feature Transform_Epub12Jul2016_GREEN AAM

    wang16dct_1_.pdf, 2.76 MB, application/pdf

    Uploaded date:11 May 2020

    Version:Accepted author manuscript

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King's Authors


Scale invariant feature transform (SIFT) is effective for representing images in computer vision tasks, as one of the most resistant feature descriptions to common image deformations. However, two issues should be addressed: first, feature description based on gradient accumulation is not compact and contains redundancies; second, multiple orientations are often extracted from one local region and therefore produce multiple descriptions, which is not good for memory efficiency. To resolve these two issues, this paper introduces a novel method to determine the dominant orientation for multiple-orientation cases, named discrete cosine transform (DCT) intrinsic orientation, and a new DCT inspired feature transform (DIFT). In each local region, it first computes a unique DCT intrinsic orientation via DCT matrix and rotates the region accordingly, and then describes the rotated region with partial DCT matrix coefficients to produce an optimized low-dimensional descriptor. We test the accuracy and robustness of DIFT on real image matching. Afterward, extensive applications performed on public benchmarks for visual retrieval show that using DCT intrinsic orientation achieves performance on a par with SIFT, but with only 60% of its features; replacing the SIFT description with DIFT reduces dimensions from 128 to 32 and improves precision. Image reconstruction resulting from DIFT is presented to show another of its advantages over SIFT.

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