2039 results about "Similarity measure" patented technology

The present invention relates to systems and/or methods that generate playlist(s) for a library or collection of media items via selecting a plurality of seed items, at least one of which is an undesirable seed item. Some of the seed items are desirable indicating that a user prefers additional media items similar to the desirable seed items and others are undesirable indicating that the user prefers additional media items dissimilar to the undesirable seed items. Additionally, the seed items can be weighted to establish a relative importance of the seed items. The invention compares media items in the collection with the seed items and determines which media items are added into the playlist by computation of similarity metrics or values. The playlist can be regenerated by adding desirable seed items to the playlist and removing media items from the playlist (e.g., undesirable seed items).

A system that incorporates an interactive graphical user interface for visualizing clusters (categories) and segments (summarized clusters) of data. Specifically, the system automatically categorizes incoming case data into clusters, summarizes those clusters into segments, determines similarity measures for the segments, scores the selected segments through the similarity measures, and then forms and visually depicts hierarchical organizations of those selected clusters. The system also automatically and dynamically reduces, as necessary, a depth of the hierarchical organization, through elimination of unnecessary hierarchical levels and inter-nodal links, based on similarity measures of segments or segment groups. Attribute/value data that tends to meaningfully characterize each segment is also scored, rank ordered based on normalized scores, and then graphically displayed. The system permits a user to browse through the hierarchy, and, to readily comprehend segment inter-relationships, selectively expand and contract the displayed hierarchy, as desired, as well as to compare two selected segments or segment groups together and graphically display the results of that comparison. An alternative discriminant-based cluster scoring technique is also presented.

A method and system are disclosed for conducting text-based searches of images using a visual signature associated with each image. A measure of string similarity between a query and an annotation associated with each entry in a first database is computed, and based upon the computed string similarity measures, a set of entries from the first database is selected. Each entry of the first database also includes an associated visual signature. At least one entry is then retrieved from a second database based upon a measure of visual similarity between a visual signature of each of the entries in the second database and the visual signatures of the entries in the selected set. Information corresponding to the retrieved entries from the second database is then generated.

The aural similarity measuring system and method provides a measure of the aural similarity between a target text (10) and one or more reference texts (11). Both the target text (10) and the reference texts (11) are converted into a string of phonemes (15) and then one or other of the phoneme strings are adjusted (16) so that both are equal in length. The phoneme strings are compared (12) and a score generated representative of the degree of similarity of the two phoneme strings. Finally, where there is a plurality of reference texts the similarity scores for each of the reference texts are ranked (13). With this aural similarity measuring system the analysis is automated thereby reducing risks of errors and omissions. Moreover, the system provides an objective measure of aural similarity enabling consistency of comparison in results and reproducibility of results.

Systems and methods enabling search of a repository for the location of data that is similar to input data, using a defined measure of similarity, in a time that is independent of the size of the repository and linear in a size of the input data, and a space that is proportional to a small fraction of the size of the repository. Additionally, remote operations are accomplished with significantly reduced system bandwidth by implementing remote differencing operations.

Methods of imaging objects based on wavelet retrieval of scenes utilize wavelet transformation of plural defined regions of a query image. By increasing the granularity of the query image to greater than one region, accurate feature vectors are obtained that allow for robust extraction of corresponding regions from a database of target images. The methods further include the use of sliding windows to decompose the query and target images into regions, and the clustering of the regions utilizing a novel similarity metric that ensures robust image matching in low response times.

A measure of similarity between two documents is computed. In computing the measure of similarity, a first list of rated keywords extracted from the first document and a second list of rated keywords extracted from the second document are received. The first and second lists of keywords are used to determine whether the first document forms part of the second document using a first computed percentage indicating what percentage of keyword ratings in the first list also exist in the second list. A second percentage is computed that indicates what percentage of keyword ratings along with a set of their neighboring keyword ratings in the first list that also exist in the second list when the first percentage indicates that the first document is included in the second document. The first percentage is used to specify the measure of similarity when the second percentage is greater than the first percentage.

The present invention is a diagnosis support system providing automated guidance to a user by automated retrieval of similar disease images and user feedback. High resolution standardized labeled and unlabeled, annotated and non-annotated images of diseased tissue in a database are clustered, preferably with expert feedback. An image retrieval application automatically computes image signatures for a query image and a representative image from each cluster, by segmenting the images into regions and extracting image features in the regions to produce feature vectors, and then comparing the feature vectors using a similarity measure. Preferably the features of the image signatures are extended beyond shape, color and texture of regions, by features specific to the disease. Optionally, the most discriminative features are used in creating the image signatures. A list of the most similar images is returned in response to a query. Keyword query is also supported.

The present invention provides, among other things, automatic identification of near-redundant units in a large TTS voice table, identifying which units are distinctive enough to keep and which units are sufficiently redundant to discard. According to an aspect of the invention, pruning is treated as a clustering problem in a suitable feature space. All instances of a given unit (e.g. word or characters expressed as Unicode strings) are mapped onto the feature space, and cluster units in that space using a suitable similarity measure. Since all units in a given cluster are, by construction, closely related from the point of view of the measure used, they are suitably redundant and can be replaced by a single instance. The disclosed method can detect near-redundancy in TTS units in a completely unsupervised manner, based on an original feature extraction and clustering strategy. Each unit can be processed in parallel, and the algorithm is totally scalable, with a pruning factor determinable by a user through the near-redundancy criterion. In an exemplary implementation, a matrix-style modal analysis via Singular Value Decomposition (SVD) is performed on the matrix of the observed instances for the given word unit, resulting in each row of the matrix associated with a feature vector, which can then be clustered using an appropriate closeness measure. Pruning results by mapping each instance to the centroid of its cluster.

A multimedia archive description scheme is provided for characterizing a multimedia archive having records and associated record descriptions. The multimedia archive description scheme provides a data structure which relates records by similarity measures. The principle data structure in the multimedia archive description scheme is a cluster. A cluster includes one or more attributes of the records in the archive and can include one or more cluster relationships. Cluster attributes can include feature space attributes, semantic attributes, media attributes and meta attributes of the records in the archive. The cluster relationships can relate records to clusters or clusters to clusters. Cluster relationships can include feature space (syntactic) relationships, semantic relationships, media relationships and meta relationships. The multimedia archive description scheme provides an efficient form for describing a collection of records.

A method for determining if one audio signal is derived from another audio signal or if two audio signals are derived from the same audio signal compares reduced-information characterizations of said audio signals, wherein said characterizations are based on auditory scene analysis. The comparison removes from the characterisations or minimizes in the characterisations the effect of temporal shift or delay on the audio signals (5-1), calculates a measure of similarity (5-2), and compares the measure of similarity against a threshold. In one alternative, the effect of temporal shift or delay is removed or minimized by cross-correlating the two characterizations. In another alternative, the effect of temporal shift or delay is removed or minimized by transforming the characterizations into a domain that is independent of temporal delay effects, such as the frequency domain. In both cases, a measure of similarity is calculated by calculating a coefficient of correlation.

First similarity values along at least four directions are ascertained within a local area containing a target pixel and weighted averaging is performed by adding the pixel values of pixels around the target pixel value to the pixel value of the target pixel, adding weight along a direction having a small first similarity value (along a direction manifesting a high degree of similarity). By incorporating the pixel value level differences among a plurality of pixels on adjacent lines extending adjacent to the target pixel into the first similarity values, it becomes possible to effectively remove jaggies that are difficult to eliminate in the prior art. Furthermore, by making a judgment on degrees of similarity by incorporating color information such as characteristics differences among different color pixels, a more accurate judgment can be made with regard to the image structure to enable very accurate direction-dependent low-pass filtering.

Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are preprocessed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.