Patent application number | Description | Published |
20090144033 | OBJECT COMPARISON, RETRIEVAL, AND CATEGORIZATION METHODS AND APPARATUSES - Object comparison is disclosed, including: adapting N universal mixture model components to a first object to generate N corresponding first object mixture model components, where N is an integer greater than or equal to two; and generating a similarity measure based on component-by-component comparison of the N first object mixture model components with corresponding N second object mixture model components obtained by adaptation of the N universal mixture model components to a second object. | 06-04-2009 |
20090231355 | COLOR TRANSFER BETWEEN IMAGES THROUGH COLOR PALETTE ADAPTATION - An image adjustment includes adapting a universal palette to generate (i) an input image palette statistically representative of pixels of an input image and (ii) a reference image palette statistically representative of pixels of a reference image, and adjusting at least some pixels of the input image to generate adjusted pixels that are statistically represented by the reference image palette. In some embodiments, a user interface for controlling the image adjustment includes a display and at least one user input device, the user interface displaying a set of colors indicative of the regions of color space represented by a palette and receiving a selection of one or more regions of the color space, so that the image adjustment adjusts those pixels of the input image lying within the one or more selected regions of the color space. | 09-17-2009 |
20090271433 | CLUSTERING USING NON-NEGATIVE MATRIX FACTORIZATION ON SPARSE GRAPHS - Object clustering techniques are disclosed. A nonnegative sparse similarity matrix is constructed for a set of objects. Nonnegative factorization of the nonnegative sparse similarity matrix is performed. Objects of the set of objects are allocated to clusters based on factor matrices generated by the nonnegative factorization of the nonnegative sparse similarity matrix. | 10-29-2009 |
20100040285 | SYSTEM AND METHOD FOR OBJECT CLASS LOCALIZATION AND SEMANTIC CLASS BASED IMAGE SEGMENTATION - An automated image processing system and method are provided for class-based segmentation of a digital image. The method includes extracting a plurality of patches of an input image. For each patch, at least one feature is extracted. The feature may be a high level feature which is derived from the application of a generative model to a representation of low level feature(s) of the patch. For each patch, and for at least one object class from a set of object classes, a relevance score for the patch, based on the at least one feature, is computed. For at least some or all of the pixels of the image, a relevance score for the at least one object class based on the patch scores is computed. An object class is assigned to each of the pixels based on the computed relevance score for the at least one object class, allowing the image to be segmented and the segments labeled, based on object class. | 02-18-2010 |
20100088073 | FAST ALGORITHM FOR CONVEX OPTIMIZATION WITH APPLICATION TO DENSITY ESTIMATION AND CLUSTERING - A method of maximizing a concave log-likelihood function comprises: selecting a pair of parameters from a plurality of adjustable parameters of a concave log-likelihood function; maximizing a value of the concave log-likelihood function respective to an adjustment value to generate an optimal adjustment value, wherein the value of one member of the selected pair of parameters is increased by the adjustment value and the value of the other member of the selected pair of parameters is decreased by the adjustment value; updating values of the plurality of adjustable parameters by increasing the value of the one member of the selected pair of parameters by the optimized adjustment value and decreasing the value of the other member of the selected pair of parameters by the optimized adjustment value; and repeating the selecting, maximizing, and updating for different pairs of parameters to identify optimized values of the plurality of adjustable parameters. | 04-08-2010 |
20100128919 | SYNCHRONIZING IMAGE SEQUENCES - As set forth herein, a computer-based method is employed to align a sequences of images. Metadata associated with images from two or more sources is received and a time stamp is extracted from the metadata. The images are sorted into sequences based at least in part upon the image source. The similarity of images from disparate sequences is measured and image pairs from disparate sequences with a similarity greater than a predetermined threshold are identified. A sequence of images is aligned by minimizing the misalignment of pairs. | 05-27-2010 |
20100159432 | SYSTEM AND METHOD FOR RECOMMENDING EDUCATIONAL RESOURCES - An educational recommender system and a method are provided. The method includes receiving a request to recommend a course of action related to a plurality of current students; accessing a computer database storing student data that corresponds to the plurality of current students; clustering in a computer process the plurality of current students into at least two clusters based at least on granular assessment data associated with student data corresponding to respective current students; and outputting the results of the clustering to a user. The granular assessment data includes a result of an assessment administered to respective students of the plurality of current students, and each assessment includes a plurality of questions for assessing one of the current students. The associated result includes an independent evaluation of each respective question of the plurality of questions. | 06-24-2010 |
20100159437 | SYSTEM AND METHOD FOR RECOMMENDING EDUCATIONAL RESOURCES - A recommender system and method is provided, including receiving a request to recommend a course of action related to a plurality of current students in accordance with a plurality of constraints and accessing a computer database storing student data that corresponds to the plurality of current students. The student data includes attribute data corresponding to respective students of the plurality of current students for describing at least one attribute related to the respective students. The method further includes clustering in a computer process the plurality of current students into a selected number of clusters based at least on sameness of attribute data corresponding to the respective current students of the plurality of current students and the plurality of constraints, and outputting the results of the clustering to a user. | 06-24-2010 |
20100159438 | SYSTEM AND METHOD FOR RECOMMENDING EDUCATIONAL RESOURCES - A recommender system and method including receiving a request identifying at least one current student for which a recommendation is sought and accessing stored student data including attributes associated with respective students of the current students and a plurality of predecessor students. For the predecessor students the attributes include educational resources. The method includes clustering one of predecessor students and the current students into clusters based on sameness of first selected attributes of their respective associated attributes. The method includes generating a mapping between respective students of the other of the predecessor and current students and one of the clusters based on sameness of second selected attributes associated with the students being mapped. The method then includes recommending for each of the current students the educational resource associated with the predecessor student or cluster of predecessor students that the current student is mapped with. | 06-24-2010 |
20100227306 | SYSTEM AND METHOD FOR RECOMMENDING EDUCATIONAL RESOURCES - An educational recommender system and a method for recommending an educational game to be used by a group of at least two students are provided. The method includes receiving a request to recommend an educational game to use with the group of students, and accessing student data relating to the at least two students including granular assessment data. The granular assessment data includes a result of at least one assessment administered to respective students, wherein each assessment includes a plurality of problems for assessing at least one of the students and the associated result includes an independent evaluation of each respective problem. The method further includes selecting an educational game that exercises the students in an academic area, including selecting the level of the academic area exercised based on granular assessment data associated with each of the respective students. | 09-09-2010 |
20100318477 | FAST AND EFFICIENT NONLINEAR CLASSIFIER GENERATED FROM A TRAINED LINEAR CLASSIFIER - A classifier method comprises: projecting a set of training vectors in a vector space to a comparison space defined by a set of reference vectors using a comparison function to generate a corresponding set of projected training vectors in the comparison space; training a linear classifier on the set of projected training vectors to generate a trained linear classifier operative in the comparison space; and transforming the trained linear classifier operative in the comparison space into a trained nonlinear classifier that is operative in the vector space to classify an input vector. | 12-16-2010 |
20110026831 | COMPACT SIGNATURE FOR UNORDERED VECTOR SETS WITH APPLICATION TO IMAGE RETRIEVAL - To compute a signature for an object comprising or represented by a set of vectors in a vector space of dimensionality D, statistics are computed that are indicative of distribution of the vectors of the set of vectors amongst a set of regions R | 02-03-2011 |
20110052063 | CONSISTENT HIERARCHICAL LABELING OF IMAGE AND IMAGE REGIONS - Classification of image regions comprises: recursively partitioning an image into a tree of image regions having the image as a tree root and at least one image patch in each leaf image region of the tree, the tree having nodes defined by the image regions and edges defined by pairs of nodes connected by edges of the tree; assigning unary classification potentials to nodes of the tree; assigning pairwise classification potentials to edges of the tree; and labeling the image regions of the tree of image regions based on optimizing an objective function comprising an aggregation of the unary classification potentials and the pairwise classification potentials. | 03-03-2011 |
20110091105 | BAGS OF VISUAL CONTEXT-DEPENDENT WORDS FOR GENERIC VISUAL CATEGORIZATION - Category context models ( | 04-21-2011 |
20110137898 | UNSTRUCTURED DOCUMENT CLASSIFICATION - A document classification method comprises: (i) classifying pages of an input document to generate page classifications; (ii) aggregating the page classifications to generate an input document representation, the aggregating not being based on ordering of the pages; and (iii) classifying the input document based on the input document representation. A page classifier for use in the page classifying operation (i) is trained based on pages of a set of labeled training documents having document classification labels. In some such embodiments, the pages of the set of labeled training documents are not labeled, and the page classifier training comprises: clustering pages of the set of labeled training documents to generate page clusters; and generating the page classifier based on the page clusters. | 06-09-2011 |
20110276872 | DYNAMIC FONT REPLACEMENT - Automated font mapping is performed for one or more document fonts of a document to map the one or more document fonts to at least one replacement font. The font mapping is limited by at least one document-specific font mapping limitation. The document is rendered using the at least one replacement font. The automated font mapping may include performing a constrained optimization of an objective function measuring similarity of the one or more document fonts and the corresponding mapped at least one replacement font, the constrained optimization being constrained by at least one constraint embodying at least one document-specific font mapping limitation. The automated font mapping may include selecting a subset of the set of fonts available for the rendering based on the at least one document-specific font mapping limitation, and performing the optimization respective to the selected subset of the set of fonts available for the rendering. | 11-10-2011 |
20110314049 | PHOTOGRAPHY ASSISTANT AND METHOD FOR ASSISTING A USER IN PHOTOGRAPHING LANDMARKS AND SCENES - A method and system to help photographers to take better quality pictures of landmarks and scenes are disclosed. A user is guided with examples of existing quality images, which are extracted from a database, of the same or similar landmarks or scenes. The method includes taking a query photograph that may include an image associated with a GPS location and other metadata, and using information extracted from the image to retrieve existing, similar images. The images retrieved may be ordered according to different criteria. When a user selects one as a model image, the user is provided with assistance for taking a target photograph of similar quality. | 12-22-2011 |
20120033874 | Learning weights of fonts for typed samples in handwritten keyword spotting - A wordspotting system and method are disclosed. The method includes receiving a keyword and, for each of a set of typographical fonts, synthesizing a word image based on the keyword. A keyword model is trained based on the synthesized word images and the respective weights for each of the set of typographical fonts. Using the trained keyword model, handwritten word images of a collection of handwritten word images which match the keyword are identified. The weights allow a large set of fonts to be considered, with the weights indicating the relative relevance of each font for modeling a set of handwritten word images. | 02-09-2012 |
20120045134 | LARGE SCALE IMAGE CLASSIFICATION - An input image representation is generated based on an aggregation of local descriptors extracted from an input image, and is adjusted by performing a power normalization, an Lp normalization such as an L2 normalization, or both. In some embodiments the generating comprises modeling the extracted local descriptors using a probabilistic model to generate the input image representation comprising probabilistic model component values for a set of probabilistic model components. In some such embodiments the probabilistic model comprises a Gaussian mixture model and the probabilistic model components comprise Gaussian components of the Gaussian mixture model. The generating may include partitioning the input image into a plurality of image partitions using a spatial pyramids partitioning model, extracting local descriptors, such as Fisher vectors, from the image partitions, and concatenating the local descriptors extracted from the image partitions. | 02-23-2012 |
20120075329 | SYSTEM AND METHOD FOR IMAGE COLOR TRANSFER BASED ON TARGET CONCEPTS - A system and method for color transfer are provided. The method includes retrieving a concept color palette from computer memory corresponding to a concept selected by a user. The concept color palette includes a first set of colors, which may be statistically representative of colors of a set of predefined color palettes which have been associated with the concept. The method further includes computing an image color palette for an input image. The image color palette includes a second set of colors that are representative of pixels of the input image. Colors of the image color palette are mapped to colors of the concept color palette to identify, for colors of the image color palette, a corresponding color in the concept color palette. A transformation is computed based on the mapping. For pixels of the input image, modified color values are computed, based on the computed transformation, to generate a modified image. | 03-29-2012 |
20120076401 | IMAGE CLASSIFICATION EMPLOYING IMAGE VECTORS COMPRESSED USING VECTOR QUANTIZATION - Local descriptors are extracted from an image. An image vector is generated having vector elements indicative of parameters of mixture model components of a mixture model representing the extracted local descriptors. The image vector is compressed using a vector quantization algorithm to generate a compressed image vector. Optionally, the compressing comprises splitting the image vector into a plurality of sub-vectors each including at least two vector elements, compressing each sub-vector independently using the vector quantization algorithm, and concatenating the compressed sub-vectors to generate the compressed image vector. Optionally, each sub-vector includes only vector elements indicative of parameters of a single mixture model component, and any sparse sub-vector whose vector elements are indicative of parameters of a mixture model component that does not represent any of the extracted local descriptors is not compressed. | 03-29-2012 |
20120143853 | LARGE-SCALE ASYMMETRIC COMPARISON COMPUTATION FOR BINARY EMBEDDINGS - A system and method for comparing a query object and one or more of a set of database objects are provided. The method includes providing quantized representations of database objects. The database objects have each been transformed with a quantized embedding function which is the composition of a real-valued embedding function and a quantization function. The query object is transformed to a representation of the query object in a real-valued embedding space using the real-valued embedding function. Query-dependent estimated distance values are computed for the query object, based on the transformed query object and stored. A comparison (e.g., distance or similarity) measure between the query object and each of the quantized database object representations is computed based on the stored query-dependent estimated distance values. Data is output based on the comparison computation. | 06-07-2012 |
20120163715 | CONVEX CLUSTERING FOR CHROMATIC CONTENT MODELING - A system and method are provided for modeling a chromatic object, such as an image. For a set of colors of a chromatic object that are expressed as color values in a perceptual color space, the method includes optimizing a convex objective function which is a log likelihood function of a combination of weighted kernels centered on each color in the set over each of the other colors in the set. A number N | 06-28-2012 |
20120269425 | PREDICTING THE AESTHETIC VALUE OF AN IMAGE - A system and method for determining the aesthetic quality of an image are disclosed. The method includes extracting a set of local features from the image, such as gradient and/or color features and generating an image representation which describes the distribution of the local features. A classifier system is used for determining an aesthetic quality of the image based on the computed image representation. | 10-25-2012 |
20130028508 | SYSTEM AND METHOD FOR COMPUTING THE VISUAL PROFILE OF A PLACE - A system and method for computing a place profile are disclosed. The method includes providing a geographical definition of a place, retrieving a set of images based on the geographical place definition. With a classifier, image-level statistics for the retrieved images are generated. The classifier has been trained to generate image-level statistics for a finite set of classes, such as different activities. The image-level statistics are aggregated to generate a place profile for the defined place which may be displayed to a user who has provided information for generating the geographical definition or used in an application such as a recommender system or to generate a personal profile for the user. | 01-31-2013 |
20130060786 | TEXT-BASED SEARCHING OF IMAGE DATA - 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. | 03-07-2013 |
20130129151 | METHODS AND SYSTEMS FOR IMPROVED LICENSE PLATE SIGNATURE MATCHING BY SIMILARITY LEARNING ON SYNTHETIC IMAGES - Methods and systems for improved license plate signature matching by similarity learning on synthetic images comprise generating a plurality of synthetic license plate images; applying one or more transformations to the synthetic license plate images to cause the synthetic license plate images to more closely resemble authentic license plate image captures; and providing the synthetic license plate images as inputs to a machine distance learning algorithm in which weighted similarity scores are calculated between signatures of analogous and non-analogous license plate images and one or more sets of signature weights are iteratively adjusted to increase the likelihood that comparing analogous license plate images results in high weighted signature similarity scores and comparing non-analogous license plate images results in low weighted signature similarity scores. | 05-23-2013 |
20130159292 | EFFICIENT DOCUMENT PROCESSING SYSTEM AND METHOD - A document processing system and method are disclosed. In the method local scores are incrementally computed for document samples, based on local features extracted from the respective sample. A global score is estimated for the document based on the local scores currently computed, i.e., on fewer than all document samples. A confidence in a decision for the estimated global score is computed. The computed confidence is based on the local scores currently computed and, optionally, the number of samples used in computing the estimated global score. A classification decision, such as a categorization or retrieval decision for the document is output, based on the estimated score when the computed confidence in the decision reaches a threshold value. | 06-20-2013 |
20130204885 | DOCUMENT PROCESSING EMPLOYING PROBABILISTIC TOPIC MODELING OF DOCUMENTS REPRESENTED AS TEXT WORDS TRANSFORMED TO A CONTINUOUS SPACE - A set of word embedding transforms are applied to transform text words of a set of documents into K-dimensional word vectors in order to generate sets or sequences of word vectors representing the documents of the set of documents. A probabilistic topic model is learned using the sets or sequences of word vectors representing the documents of the set of documents. The set of word embedding transforms are applied to transform text words of an input document into K-dimensional word vectors in order to generate a set or sequence of word vectors representing the input document. The learned probabilistic topic model is applied to assign probabilities for topics of the probabilistic topic model to the set or sequence of word vectors representing the input document. A document processing operation such as annotation, classification, or similar document retrieval may be performed using the assigned topic probabilities. | 08-08-2013 |
20130290222 | RETRIEVAL SYSTEM AND METHOD LEVERAGING CATEGORY-LEVEL LABELS - An instance-level retrieval method and system are provided. A representation of a query image is embedded in a multi-dimensional space using a learned projection. The projection is learned using category-labeled training data to optimize a classification rate on the training data. The joint learning of the projection and the classifiers improves the computation of similarity/distance between images by embedding them in a subspace where the similarity computation outputs more accurate results. An input query image can thus be used to retrieve similar instances in a database by computing the comparison measure in the embedding space. | 10-31-2013 |
20130336538 | Occupancy detection for managed lane enforcement based on localization and classification of windshield images - A system for detecting a vehicle occupancy violation includes an image capture module that acquires an image including a vehicle cabin from a camera positioned to view oncoming traffic. The system includes a violation determination device, which includes a feature extraction module that processes the image pixels for determining an image descriptor. The process is selected from a group consisting of a Successive Mean Quantization Transform; a Scale-Invariant Feature Transform; a Histogram of Gradients; a Bag-of-Visual-Words Representation; a Fisher Vector Representation; and, a combination of the above. The system further includes a classifier that determines a distance that the vehicle image descriptor/representation is positioned in the projected feature space relative to a hyper-plane. The classifier determines whether the distance meets a threshold and classifies the image when the threshold is met. A processor implements the modules. A graphic user interface outputs the classification. | 12-19-2013 |
20130339386 | PRIVACY PRESERVING METHOD FOR QUERYING A REMOTE PUBLIC SERVICE - A system and a method of querying a remote service without revealing a private document to the remote service are provided. The method includes receiving a signature of a user's private document, and querying an intermediate database with the signature of the private document to generate an intermediate result set comprising intermediate database documents, based on a computation of similarity of the signatures of the intermediate database documents to the signature of the private document. The remote service is queried, based on the intermediate result set and a final result set is received from the remote service based on the query, which can be output to the user or further processed. | 12-19-2013 |
20140029839 | METRIC LEARNING FOR NEAREST CLASS MEAN CLASSIFIERS - A classification system and method enable improvements to classification with nearest class mean classifiers by computing a comparison measure between a multidimensional representation of a new sample and a respective multidimensional class representation embedded into a space of lower dimensionality than that of the multidimensional representations. The embedding is performed with a projection that has been learned on labeled samples to optimize classification with respect to multidimensional class representations for classes which may be the same or different from those used subsequently for classification. Each multidimensional class representation is computed as a function of a set of multidimensional representations of labeled samples, each labeled with the respective class. A class is assigned to the new sample based on the computed comparison measures. | 01-30-2014 |
20140347511 | METHODS AND SYSTEMS FOR CONFIDENCE-BASED IMAGE PROCESSING - A system and method for triggering image re-capture in image processing by receiving a first image captured using a first mode, performing a computer vision task on the first image to produce a first result, generating a confidence score of the first result using a machine learning technique, triggering an image re-capture using a second mode in response to the confidence score of the first result, and performing the computer vision task on a result of the image recapture using the second mode. | 11-27-2014 |
20140351264 | METHODS AND SYSTEMS FOR RANKING IMAGES USING SEMANTIC AND AESTHETIC MODELS - A method, a system, and a computer program product for extracting one or more images from a storage medium. A search model is selected based on the availability of a semantically related aesthetic model. A search model includes a generic aesthetic model if the semantically related aesthetic model for query is not available. A semantic score and an aesthetic score are computed based on the selected search model. The images are further ranked based on the semantic and aesthetic score. | 11-27-2014 |