Feature selection for low bit rate mobile augmented reality applications

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Journal Article
Signal Processing: Image Communication, 2015, 36 pp. 115 - 126
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© 2015 Elsevier B.V. Abstract Mobile augmented reality applications rely on automatically matching a captured visual scene to an image in a database. This is typically achieved by deriving a set of features from the captured image, transmitting them through a network and then matching with features derived from a database of reference images. A fundamental problem is to select as few and robust features as possible such that the matching accuracy is invariant to distortions caused by camera capture whilst minimizing the bit rate required for their transmission. In this paper, novel feature selection methods are proposed, based on the entropy of the image content, entropy of extracted features and the Discrete Cosine Transformation (DCT) coefficients. The methods proposed in the descriptor domain and DCT domain achieve better retrieval accuracy under low bit rate transmission than state-of-the-art peak based feature selection used within the MPEG-7 Compact Descriptor for Visual Search (CDVS). The robustness of the proposed methods is evaluated under controlled single distortion and the retrieval performance is verified from image retrieval experiments and results for a realistic dataset with complex real world capturing distortion. Results show that the proposed method can improve the matching accuracy for various detectors. On the one side, feature selection can achieve low bit rate transmission, on the other side, when coping with distorted images, it can result in a higher matching accuracy than using all features. Hence, even if all the features can be transmitted in high transmission bandwidth scenarios, feature selection should still be applied to the distorted query image to ensure high matching accuracy.
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