The principle that, for optimal retrieval, documents should be ranked in order of the probability of relevance or usefulness has been brought into question by Cooper. It is shown that the principle can be justified under certain assumptions, but that in cases where these assumptions do not hold, the principle is not valid. The major problem appears to lie in the way the principle considers each document independently of the rest. The nature of the information on the basis of which the system decides whether or not to retrieve the documents determines whether the document‐by‐document approach is valid.

The probability ranking principle in IR

A system has an image store, a digital hashing unit, and a watermark encoder. A digital image hashing unit computes a hash value representative of a digital image in such a manner that visually similar images hash to the same hash value and visually distinct images hash to different values. The hash value is stored in an image hash table and is associated via the table with the original image. This image hash table can be used to index the image storage. A watermark encoder computes a watermark based on the hash value and a secret. Using both values renders the watermark resistant to BORE (Break Once, Run Everywhere) attacks because even if the global watermark secret is discovered, an attacker still needs the hash value of each image to successfully attack the image. The system can be configured to police the Internet to detect pirated copies. The system randomly collects images from remote Web sites and hashes the images using the same hashing function. The system then compares the image hashes to hashes of the original images. If the hashes match, the collected image is suspected as being a copy of the original.

System and method for hashing digital images

An implementation of a technology is described herein for recognizing the perceptual similarity of the content of digital goods. At least one implementation, described herein, introduces a new hashing technique. More particularly, this hashing technique produces hash values for digital goods that are proximally near each other, when the digital goods contain perceptually similar content. In other words, if the content of digital goods are perceptually similar, then their hash values are, likewise, similar. The hash values are proximally near each other. This is unlike conventional hashing techniques where the hash values of goods with perceptually similar content are far apart with high probability in some distance sense (e.g., Hamming). This abstract itself is not intended to limit the scope of this patent. The scope of the present invention is pointed out in the appending claims.

Robust recognizer of perceptually similar content

An implementation of a technology is described herein for recognizing the audio content of digital signals. The technology determines one or more hash values for the original audio content of a digital signal. In one implementation described herein, audio content recognition facilitates identification of the original audio content and other audio content that is substantially perceptually same to the original audio content. In another implementation described herein, audio content recognition facilitates semantic categorization of such original content so that it may be grouped with other audio works in the same semantic category. This abstract itself is not intended to limit the scope of this patent. The scope of the present invention is pointed out in the appending claims.

Recognizer of audio-content in digital signals

An implementation of a technology is described herein for deriving robust non-local characteristics and quantizing such characteristics for blind watermarking of a digital good.

Derivation and quantization of robust non-local characteristics for blind watermarking

The test sequence compaction problem is modeled here, first, as a set covering problem. This formulation enables the straightforward application of set covering methods for compaction. Because of the complexity inherent in the first model, a second more efficient, formulation is proposed where the test sequences are modeled as matrix columns with variable costs (number of vectors). Further, matrix reduction rules appropriate to the new formulation, which do not affect the optimality of the solution, are introduced. Finally, the reduced problem is minimized with a Branch & Bound algorithm. Experiments on a large number of test sets show significant reductions to the original problem by simply using the presented reduction rules. Experimental results comparing our method with others from the literature and also with the absolute minima of the examples, computed separately with the MINCOV algorithm, support the potential of the proposed approach.

Efficient static compaction of test sequence sets through the application of set covering techniques

Comparison of signature file models with superimposed coding

This paper describes a method of content indexing for dynamic multimedia databases using binary signatures of the audio signals. The analysis scheme focuses on the audio objects of the multimedia environment, extracts semi-stationary audio segments and analyzes their key features, These are: the fundamental frequency F0, the first three formants F1, F2, and F3, the associated phoneme and speaker identification information. Binary signatures are associated with the audio segments which they describe. For each audio segment these key features are encoded within the signature and information retrieval can be performed on the multimedia database querying the corresponding signatures of the streamed audio. The signature structure serves as an index of the associated audio objects assisting their retrieval.

An audio signatures indexing scheme for dynamic content multimedia databases

The problem of compacting a set of test sequences for sequential circuits is modeled here with the help of a covering matrix, where the test sequences are modeled as columns with variable cost to reflect the cost (number of vectors) of covering selected subsets of circuit faults. From this formulation, reduction rules are extracted, particular to this type of problem which, iteratively applied, result in a significant reduction of the size of the initial compaction problem. A characteristic of the reduction rules is that their application will not compromise the optimum solution of the problem. The remaining reduced problem is then solved by a combination of a heuristic and an exact branch and bound algorithm. Experimental results using the above reduction rules show that the sizes of the given sets of test sequences are often significantly reduced and many times these rules directly produce the absolute minimum of the solution. The final results, compared with others from the literature and also with the absolute minima of the examples, computed separately, support the potential of the proposed approach.

Panagiotis Linardis

Papers

Efficient static compaction of test sequence sets through the application of set covering techniques

Comparison of signature file models with superimposed coding

An audio signatures indexing scheme for dynamic content multimedia databases

Accelerating the compaction of test sequences in sequential circuits through problem size reduction

VIDI - A Voice Instrument Digital Interface for Byzantine Music.