2,487 research outputs found
Vectors of Locally Aggregated Centers for Compact Video Representation
We propose a novel vector aggregation technique for compact video
representation, with application in accurate similarity detection within large
video datasets. The current state-of-the-art in visual search is formed by the
vector of locally aggregated descriptors (VLAD) of Jegou et. al. VLAD generates
compact video representations based on scale-invariant feature transform (SIFT)
vectors (extracted per frame) and local feature centers computed over a
training set. With the aim to increase robustness to visual distortions, we
propose a new approach that operates at a coarser level in the feature
representation. We create vectors of locally aggregated centers (VLAC) by first
clustering SIFT features to obtain local feature centers (LFCs) and then
encoding the latter with respect to given centers of local feature centers
(CLFCs), extracted from a training set. The sum-of-differences between the LFCs
and the CLFCs are aggregated to generate an extremely-compact video description
used for accurate video segment similarity detection. Experimentation using a
video dataset, comprising more than 1000 minutes of content from the Open Video
Project, shows that VLAC obtains substantial gains in terms of mean Average
Precision (mAP) against VLAD and the hyper-pooling method of Douze et. al.,
under the same compaction factor and the same set of distortions.Comment: Proc. IEEE International Conference on Multimedia and Expo, ICME
2015, Torino, Ital
Twofold Video Hashing with Automatic Synchronization
Video hashing finds a wide array of applications in content authentication,
robust retrieval and anti-piracy search. While much of the existing research
has focused on extracting robust and secure content descriptors, a significant
open challenge still remains: Most existing video hashing methods are fallible
to temporal desynchronization. That is, when the query video results by
deleting or inserting some frames from the reference video, most existing
methods assume the positions of the deleted (or inserted) frames are either
perfectly known or reliably estimated. This assumption may be okay under
typical transcoding and frame-rate changes but is highly inappropriate in
adversarial scenarios such as anti-piracy video search. For example, an illegal
uploader will try to bypass the 'piracy check' mechanism of YouTube/Dailymotion
etc by performing a cleverly designed non-uniform resampling of the video. We
present a new solution based on dynamic time warping (DTW), which can implement
automatic synchronization and can be used together with existing video hashing
methods. The second contribution of this paper is to propose a new robust
feature extraction method called flow hashing (FH), based on frame averaging
and optical flow descriptors. Finally, a fusion mechanism called distance
boosting is proposed to combine the information extracted by DTW and FH.
Experiments on real video collections show that such a hash extraction and
comparison enables unprecedented robustness under both spatial and temporal
attacks.Comment: submitted to Image Processing (ICIP), 2014 21st IEEE International
Conference o
Perceptual Video Hashing for Content Identification and Authentication
Perceptual hashing has been broadly used in the literature to identify similar contents for video copy detection. It has also been adopted to detect malicious manipulations for video authentication. However, targeting both applications with a single system using the same hash would be highly desirable as this saves the storage space and reduces the computational complexity. This paper proposes a perceptual video hashing system for content identification and authentication. The objective is to design a hash extraction technique that can withstand signal processing operations on one hand and detect malicious attacks on the other hand. The proposed system relies on a new signal calibration technique for extracting the hash using the discrete cosine transform (DCT) and the discrete sine transform (DST). This consists of determining the number of samples, called the normalizing shift, that is required for shifting a digital signal so that the shifted version matches a certain pattern according to DCT/DST coefficients. The rationale for the calibration idea is that the normalizing shift resists signal processing operations while it exhibits sensitivity to local tampering (i.e., replacing a small portion of the signal with a different one). While the same hash serves both applications, two different similarity measures have been proposed for video identification and authentication, respectively. Through intensive experiments with various types of video distortions and manipulations, the proposed system has been shown to outperform related state-of-the art video hashing techniques in terms of identification and authentication with the advantageous ability to locate tampered regions
DC-image for real time compressed video matching
This chapter presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without full decompression. In addition, the relevant arguments and supporting evidences are discussed. Several local feature detectors will be examined to select the best for matching using the DC-image. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and computation complexity. The second experiment compares between using local features and global features regarding compressed video matching with respect to the DC-image. The results confirmed that the use of DC-image, despite its highly reduced size, it is promising as it produces higher matching precision, compared to the full I-frame. Also, SIFT, as a local feature, outperforms most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin which leaves a space for further optimizations that can be done to improve this computation complexity
A quick search method for audio signals based on a piecewise linear representation of feature trajectories
This paper presents a new method for a quick similarity-based search through
long unlabeled audio streams to detect and locate audio clips provided by
users. The method involves feature-dimension reduction based on a piecewise
linear representation of a sequential feature trajectory extracted from a long
audio stream. Two techniques enable us to obtain a piecewise linear
representation: the dynamic segmentation of feature trajectories and the
segment-based Karhunen-L\'{o}eve (KL) transform. The proposed search method
guarantees the same search results as the search method without the proposed
feature-dimension reduction method in principle. Experiment results indicate
significant improvements in search speed. For example the proposed method
reduced the total search time to approximately 1/12 that of previous methods
and detected queries in approximately 0.3 seconds from a 200-hour audio
database.Comment: 20 pages, to appear in IEEE Transactions on Audio, Speech and
Language Processin
Integration and optimization of collusion secure fingerprinting in image watermarking
Estágio realizado na Fraunhofer SIT - e orientado pelo Dr. Huajian Liu e pelo Dr. Marcel SchäferTese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201
Fast fallback watermark detection using perceptual hashes
Forensic watermarking is often used to enable the tracing of digital pirates that leak copyright-protected videos. However, existing watermarking methods have a limited robustness and may be vulnerable to targeted attacks. Our previous work proposed a fallback detection method that uses secondary watermarks rather than the primary watermarks embedded by existing methods. However, the previously proposed fallback method is slow and requires access to all watermarked videos. This paper proposes to make the fallback watermark detection method faster using perceptual hashes instead of uncompressed secondary watermark signals. These perceptual hashes can be calculated prior to detection, such that the actual detection process is sped up with a factor of approximately 26,000 to 92,000. In this way, the proposed method tackles the main criticism about practical usability of the slow fallback method. The fast detection comes at the cost of a modest decrease in robustness, although the fast fallback detection method can still outperform the existing primary watermark method. In conclusion, the proposed method enables fast and more robust detection of watermarks that were embedded by existing watermarking methods
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