Most video hashing algorithms have the common pipeline, which consists of feature extraction and hash mapping. The performance of video hash is usually promoted via the improvement in one or both of aspects. In this paper, a learningbased video feature is used, which is obtained via a 3D-CNN model. The 3DCNN-based features can represent both spatial and temporal information of videos, as 3D convolutions used in 3DCNN can capture the motion information through multiple adjacent frames. A video hashing algorithm is proposed based on the 3DCNN-based feature, which is defined as CNNF. In addition, the hash length optimization method is used to get the approximately optimal hash length in hash mapping stage of the proposed algorithm. Since the feature extraction and hash length optimization are independent to hash mapping algorithms, several classical hashing algorithms are adopted to verify the improvements of these two aspects via the video copy detection task. Experiments demonstrate the performance of the proposed CNNFHash algorithm.
Eye movement is a new kind of feature for biometrical recognition, it has many advantages compared with other features such as fingerprint, face, and iris. It is not only a sort of static characteristics, but also a combination of brain activity and muscle behavior, which makes it effective to prevent spoofing attack. In addition, eye movements can be incorporated with faces, iris and other features recorded from the face region into multimode systems. In this paper, we do an exploring study on eye movement identification based on the eye movement datasets provided by Komogortsev et al. in 2011 with different classification methods. The time of saccade and fixation are extracted from the eye movement data as the eye movement features. Furthermore, the performance analysis was conducted on different classification methods such as the BP, RBF, ELMAN and SVM in order to provide a reference to the future research in this field.
Keratoconus is a progressive cornea disease that can lead to serious myopia and astigmatism, or even to corneal transplantation, if it becomes worse. The early detection of keratoconus is extremely important to know and control its condition. In this paper, we propose an automatic diagnosis algorithm for keratoconus to discriminate the normal eyes and keratoconus ones. We select the parameters obtained by Oculyzer as the feature of cornea, which characterize the cornea both directly and indirectly. In our experiment, 289 normal cases and 128 keratoconus cases are divided into training and test sets respectively. Far better than other kernels, the linear kernel of SVM has sensitivity of 94.94% and specificity of 97.87% with all the parameters training in the model. In single parameter experiment of linear kernel, elevation with 92.03% sensitivity and 98.61% specificity and thickness with 97.28% sensitivity and 97.82% specificity showed their good classification abilities. Combining elevation and thickness of the cornea, the proposed method can reach 97.43% sensitivity and 99.19% specificity. The experiments demonstrate that the proposed automatic diagnosis method is feasible and reliable.
This paper proposed a single image super-resolution algorithm based on image patch classification and sparse representation where gradient information is used to classify image patches into three different classes in order to reflect the difference between the different types of image patches. Compared with other classification algorithms, gradient information based algorithm is simpler and more effective. In this paper, each class is learned to get a corresponding sub-dictionary. High-resolution image patch can be reconstructed by the dictionary and sparse representation coefficients of corresponding class of image patches. The result of the experiments demonstrated that the proposed algorithm has a better effect compared with the other algorithms.
In the quantization-based watermarking framework, the perceptual just noticeable distortion (JND) model has been widely used to determine the quantization step size, as it can provide a better tradeoff between fidelity and robustness. However, the calculated JND values can vary due to changes introduced by watermark embedding. As a result, the mismatch problem will lead to watermark extraction errors in the absence of attacks. We present an improved spread transform dither modulation (STDM) watermarking scheme. Performance improvement with respect to the existing algorithm is obtained by a discrete cosine transform (DCT)-based perceptual JND model that is highly compatible with the STDM watermarking algorithm. The proposed scheme not only incorporates various masking effects of human visual perception, but also avoids the mismatch problem by utilizing a new measurement of the pixel intensity and edge strength. In contrast to conventional JND models, the proposed model can be theoretically invariant to the changes in the watermark-embedding processing, therefore, more fit for quantization-based watermarking. Experimental results confirm the improved robustness performance of the JND model in the STDM watermarking framework. Simulation results show that the proposed scheme is more robust than the existing JND model-based watermarking algorithms with uniform fidelity. Furthermore, our proposed scheme has a superior performance compared with previously proposed perceptual STDM schemes.