Abstract: In one embodiment, a method includes receiving a query, wherein the query is represented by an n-dimensional vector in an n-dimensional vector space; quantizing the vector representing the query using a quantizer, wherein the quantized vector corresponds to a polysemous code, and wherein the quantizer has been trained by machine learning to determine polysemous codes such that the Hamming distance approximates the inter-centroid distance using an objective function; calculating, for each of a plurality of content objects, a Hamming distance between the polysemous code corresponding to the vector representing the query and a polysemous code corresponding to a quantized vector representing the content object; and determining that a content object of the plurality of content objects is an approximate nearest neighbor to the query based on determining that the calculated Hamming distance is less than a threshold amount.

Abstract: A privacy-preserving multi-query parking management system and method. An image of a vehicle with respect to a specific part (e.g., license plate) can be captured when the vehicle enters via an entry point. An image signature can be computed by embedding the image into a descriptor space and the image signature can be stored in a database together with relevant metadata. An image of a vehicle with respect to the specific part can be captured when the vehicle exits via at least one exit point and an image signature can be computed. An entry-exit association with respect to the vehicle can be performed by searching for a most similar image signature in the database. Additionally, given a textual query (e.g., a license plate number), a signature can be computed by embedding the text into the same descriptor space.

Abstract: A method, system, and apparatus for vehicle occupancy detection comprises collecting image data of a vehicle, creating a first value according to a plurality of characteristics of a driver position of the vehicle, and then creating at least one other value according to a plurality of characteristics of at least one other candidate occupant position in the vehicle. The characteristics of the driver position of the vehicle and candidate occupant position of the vehicle are compared in order to determine the vehicle occupancy.

Abstract: 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.

Abstract: A system and method for comparing a text image with or without a wildcard character and a character string are provided. The method includes embedding a character string into a vectorial space by extracting a set of features from the character string and generating a character string representation based on the extracted features, such as a spatial pyramid bag of characters (SPBOC) representation. A text image is embedded into a vectorial space by extracting a set of features from the text image and generating a text image representation based on the text image extracted features. A similarity between the text image representation and the character string representation is computed, which includes computing a function of the text image representation and character string representation.

Abstract: 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.

Abstract: A system and method for comparing a text image with or without a wildcard character and a character string are provided. The method includes embedding a character string into a vectorial space by extracting a set of features from the character string and generating a character string representation based on the extracted features, such as a spatial pyramid bag of characters (SPBOC) representation. A text image is embedded into a vectorial space by extracting a set of features from the text image and generating a text image representation based on the text image extracted features. A similarity between the text image representation and the character string representation is computed, which includes computing a function of the text image representation and character string representation.

Abstract: 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.

Abstract: A privacy-preserving multi-query parking management system and method. An image of a vehicle with respect to a specific part (e.g., license plate) can be captured when the vehicle enters via an entry point. An image signature can be computed by embedding the image into a descriptor space and the image signature can be stored in a database together with relevant metadata. An image of a vehicle with respect to the specific part can be captured when the vehicle exits via at least one exit point and an image signature can be computed. An entry-exit association with respect to the vehicle can be performed by searching for a most similar image signature in the database. Additionally, given a textual query (e.g., a license plate number), a signature can be computed by embedding the text into the same descriptor space.

Abstract: 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.

Abstract: 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.

Abstract: A method, system, and apparatus for vehicle occupancy detection comprises collecting image data of a vehicle, creating a first value according to a plurality of characteristics of a driver position of the vehicle, and then creating at least one other value according to a plurality of characteristics of at least one other candidate occupant position in the vehicle. The characteristics of the driver position of the vehicle and candidate occupant position of the vehicle are compared in order to determine the vehicle occupancy.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.