access icon free Image retrieval based on ASIFT features in a Hadoop clustered system

For image matching, the scale invariant feature transform (SIFT) algorithm is a commonly used one. They are invariant to image rotation, scale zooming, and partially invariant to change in illumination and 3D camera viewpoint. Affine SIFT (ASIFT) is an extension of SIFT, which solves the problem when images are captured at different angles. However, ASIFT has higher computational complexity than SIFT, due to a huge amount of features in the images. Therefore, in this study, a Hadoop-based image retrieval system is proposed to solve the ASIFT shortcomings of high computation by the MapReduce technology. The system uses a combination of the Bag-of-Words method and support vector machine. Finally, the experimental results verify that the proposed method is more effective than the other state-of-the-art methods for a variety of datasets.

Inspec keywords: computational complexity; support vector machines; feature extraction; stereo image processing; affine transforms; image retrieval; image matching; parallel processing

Other keywords: scale zooming; higher computational complexity; support vector machine; 3D camera viewpoint; MapReduce technology; affine SIFT; image matching; image rotation; Hadoop clustered system; bag-of-words method; Hadoop-based image retrieval system; scale invariant feature transform algorithm; ASIFT features

Subjects: Optical, image and video signal processing; Knowledge engineering techniques; Information retrieval techniques; Integral transforms; Integral transforms; Computer vision and image processing techniques; Computational complexity; Multiprocessing systems

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