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Abstract: In this paper we introduce an incremental non-negative matrix factorization (INMF) scheme in order to overcome the difficulties that conventional NMF has in online processing of large data sets. The proposed scheme enables incrementally updating its factors by reflecting the influence of each observation on the factorization appropriately. This is achieved via a weighted cost function which also allows controlling the memorylessness of the factorization. Unlike conventional NMF, with its incremental nature and weighted cost function the INMF scheme successfully utilizes adaptability to dynamic data content changes with a lower computational complexity. Test results reported for two video applications, namely background modeling in video surveillance and clustering, demonstrate that INMF is capable of online representing data content while reducing dimension significantly. [Copyright &y& Elsevier]

This paper introduces two spatial methods in order to embed watermark data into fingerprint images, without corrupting their features. The first method inserts watermark data after feature extraction, thus preventing watermarking of regions used for fingerprint classification. The method utilizes an image adaptive strength adjustment technique which results in watermarks with low visibility. The second method introduces a feature adaptive watermarking technique for fingerprints, thus applicable before feature extraction. For both of the methods, decoding does not require original fingerprint image. Unlike most of the published spatial watermarking methods, the proposed methods provide high decoding accuracy for fingerprint images. High data hiding and decoding performance for color images is also observed. [Copyright &y& Elsevier]

Abstract: Conventional blind audio watermark (WM) decoders use matched-filtering techniques because of their simplicity. In these methods, WM decoding and WM detection are often considered as separate problems and the WM signal embedded by spreading a secret key through the spectrum of a host signal is extracted by maximizing correlation between the secret key and the received audio. Conventionally decoding is achieved by using a pre-defined decoding/detection threshold and tradeoff between the false rejection ratio and false acceptance ratio constitutes main drawback of the conventional decoders. Unlike the conventional methods, this paper introduces a pattern recognition (PR) framework to WM extraction and integrates WM decoding and detection problems into a unique classification problem that eliminates thresholding. The proposed method models statistics of watermarked and original audio signals by a Gaussian mixture model (GMM) with K components. Learning of the embedded WM data is achieved in a principal component analysis (PCA) transformed wavelet space and a maximum likelihood (ML) classifier is designed for WM decoding. Robustness of the proposed method is evaluated under compression, additive noise and Stirmark benchmark attacks. It is shown that both WM decoding and detection performances of the introduced decoder outperform the conventional decoders. [Copyright &y& Elsevier]