Abstract: In a method for detecting an object in an input image, an input image vector representing the input image is generated by performing a regional maximum activations of convolutions (R-MAC) using a convolutional neural network (CNN) applied to the input image and using regions for the R-MAC defined by applying a region proposal network (RPN) to the output of the CNN applied to the input image. Likewise, a reference image vector representing a reference image depicting the object is generated by performing the R-MAC using the CNN applied to the reference image and using regions for the R MAC defined by applying the RPN to the output of the CNN applied to the reference image. A similarity metric between the input image vector and the reference image vector is computed, and the object is detected as present in the input image if the similarity metric satisfies a detection criterion.

Abstract: A system and method enable semantic comparisons to be made between word images and concepts. Training word images and their concept labels are used to learn parameters of a neural network for embedding word images and concepts in a semantic subspace in which comparisons can be made between word images and concepts without the need for transcribing the text content of the word image. The training of the neural network aims to minimize a ranking loss over the training set where non relevant concepts for an image which are ranked more highly than relevant ones penalize the ranking loss.

Abstract: Similar images are identified by semantically matching human-supplied text captions accompanying training images. An image representation function is trained to produce similar vectors for similar images according to this similarity. The trained function is applied to non-training second images in a different database to produce second vectors. This trained function does not require the second images to contain captions. A query image is matched to the second images by applying the trained function to the query image to produce a query vector, and the second images are ranked based on how closely the second vectors match the query vector, and the top ranking ones of the second images are output as a response to the query image.

Abstract: A method for query expansion uses a representation of an input query object, such as an image, to retrieve representations of similar objects retrieved using the query object representation as a query. Given the set of image representations, a weight is predicted for each using a prediction model which assigns different weights to the image representations. An expanded query is generated as a weighted aggregation (e.g., sum) of the query object representation and at least a subset of the set of similar object representations in which each object representation is weighted with its predicted weight. A higher weight can thus be given to one of the similar object representations, in the expanded query, than to another.

Abstract: In a method for detecting an object in an input image, an input image vector representing the input image is generated by performing a regional maximum activations of convolutions (R-MAC) using a convolutional neural network (CNN) applied to the input image and using regions for the R-MAC defined by applying a region proposal network (RPN) to the output of the CNN applied to the input image. Likewise, a reference image vector representing a reference image depicting the object is generated by performing the R-MAC using the CNN applied to the reference image and using regions for the R MAC defined by applying the RPN to the output of the CNN applied to the reference image. A similarity metric between the input image vector and the reference image vector is computed, and the object is detected as present in the input image if the similarity metric satisfies a detection criterion.

Abstract: A method is disclosed for performing multiple classification of an image simultaneously using multiple classifiers, where information between the classifiers is shared explicitly and is achieved with a low-rank decomposition of the classifier weights. The method includes applying an input image to classifiers and, more particularly, multiplying the extracted input image features by |?| embedding matrices ?c to generate a latent representation of d-dimensions for each of the |?| characters. The embedding matrices are uncorrelated with a position of the extracted character. The step of applying the extracted character to the classifiers further includes projecting the latent representation with a decoding matrix shared by all the character embedding matrices to generate scores of every character in an alphabet at every position. At least one of the multiplying the extracted input image features and the projecting the latent representation with the decoding matrix are performed with a processor.

Abstract: Methods and systems recognize alphanumeric characters in an image by computing individual representations of every character of an alphabet at every character position within a certain word transcription length. These methods and systems embed the individual representations of each alphabet character in a common vectorial subspace (using a matrix) and embed a received image of an alphanumeric word into the common vectorial subspace (using the matrix). Such methods and systems compute the utility value of the embedded alphabet characters at every one of the character positions with respect to the embedded alphanumeric character image; and compute the best transcription alphabet character of every one of the image characters based on the utility value of each embedded alphabet character at each character position. Such methods and systems then assign the best transcription alphabet character for each of the character positions to produce a recognized alphanumeric word within the received image.

Abstract: A method for extracting a representation from an image includes inputting an image to a pre-trained neural network. The gradient of a loss function is computed with respect to parameters of the neural network, for the image. A gradient representation is extracted for the image based on the computed gradients, which can be used, for example, for classification or retrieval.

Abstract: Methods and systems for license plate recognition utilizing a trained neural network. In an example embodiment, a neural network can be subject to operations involving iteratively training and adapting the neural network for a particular task such as, for example, text recognition in the context of a license plate recognition application. The neural network can be trained to perform generic text recognition utilizing a plurality of training samples. The neural network can be applied to a cropped image of a license plate in order to recognize text and produce a license plate transcription with respect to the license plate. An example of such a neural network is a CNN (Convolutional Neural. Network).

Abstract: A system and method provide object localization in a query image based on a global representation of the image generated with a model derived from a convolutional neural network. Representations of annotated images and a query image are each generated based on activations output by a layer of the model which precedes the fully-connected layers of the neural network. A similarity is computed between the query image representation and each of the annotated image representations to identify a subset of the annotated images having the highest computed similarity. Object location information from at least one of the subset of annotated images is transferred to the query image and information is output, based on the transferred object location information.

Abstract: Authentication methods are disclosed for determining whether a person or object to be authenticated is a member of a set of authorized persons or objects. A query signature is acquired comprising a vector whose elements store values of an ordered set of features for the person or object to be authenticated. The query signature is compared with an aggregate signature comprising a vector whose elements store values of the ordered set of features for the set of authorized persons or objects. The individual signatures for the authorized persons or objects are not stored; only the aggregate signature. It is determined whether the person or object to be authenticated is a member of the set of authorized persons or objects based on the comparison. The comparing may comprise computing an inner product of the query signature and the aggregate signature, with the determining being based on the inner product.

Abstract: Methods and systems for license plate recognition utilizing a trained neural network. In an example embodiment, a neural network can be subject to operations involving iteratively training and adapting the neural network for a particular task such as, for example, text recognition in the context of a license plate recognition application. The neural network can be trained to perform generic text recognition utilizing a plurality of training samples. The neural network can be applied to a cropped image of a license plate in order to recognize text and produce a license plate transcription with respect to the license plate. An example of such a neural network is a CNN (Convolutional Neural Network).

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 system and method provide object localization in a query image based on a global representation of the image generated with a model derived from a convolutional neural network. Representations of annotated images and a query image are each generated based on activations output by a layer of the model which precedes the fully-connected layers of the neural network. A similarity is computed between the query image representation and each of the annotated image representations to identify a subset of the annotated images having the highest computed similarity. Object location information from at least one of the subset of annotated images is transferred to the query image and information is output, based on the transferred object location information.

Abstract: A system and method enable semantic comparisons to be made between word images and concepts. Training word images and their concept labels are used to learn parameters of a neural network for embedding word images and concepts in a semantic subspace in which comparisons can be made between word images and concepts without the need for transcribing the text content of the word image. The training of the neural network aims to minimize a ranking loss over the training set where non relevant concepts for an image which are ranked more highly than relevant ones penalize the ranking loss.

Abstract: A method for extracting a representation from an image includes inputting an image to a pre-trained neural network. The gradient of a loss function is computed with respect to parameters of the neural network, for the image. A gradient representation is extracted for the image based on the computed gradients, which can be used, for example, for classification or retrieval.

Abstract: Methods and systems recognize alphanumeric characters in an image by computing individual representations of every character of an alphabet at every character position within a certain word transcription length. These methods and systems embed the individual representations of each alphabet character in a common vectorial subspace (using a matrix) and embed a received image of an alphanumeric word into the common vectorial subspace (using the matrix). Such methods and systems compute the utility value of the embedded alphabet characters at every one of the character positions with respect to the embedded alphanumeric character image; and compute the best transcription alphabet character of every one of the image characters based on the utility value of each embedded alphabet character at each character position. Such methods and systems then assign the best transcription alphabet character for each of the character positions to produce a recognized alphanumeric word within the received image.

Abstract: Authentication methods are disclosed for determining whether a person or object to be authenticated is a member of a set of authorized persons or objects. A query signature is acquired comprising a vector whose elements store values of an ordered set of features for the person or object to be authenticated. The query signature is compared with an aggregate signature comprising a vector whose elements store values of the ordered set of features for the set of authorized persons or objects. The individual signatures for the authorized persons or objects are not stored; only the aggregate signature. It is determined whether the person or object to be authenticated is a member of the set of authorized persons or objects based on the comparison. The comparing may comprise computing an inner product of the query signature and the aggregate signature, with the determining being based on the inner product.

Abstract: A system and method for object instance localization in an image are disclosed. In the method, keypoints are detected in a target image and candidate regions are detected by matching the detected keypoints to keypoints detected in a set of reference images. Similarity measures between global descriptors computed for the located candidate regions and global descriptors for the reference images are computed and labels are assigned to at least some of the candidate regions based on the computed similarity measures. Performing the region detection based on keypoint matching while performing the labeling based on global descriptors improves object instance detection.

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.