In some examples, it is disclosed a method for generating an image retrieval system configured to rank a plurality of images of cargo from a dataset of images, in response to a query corresponding to an image of cargo of interest generated using penetrating radiation. The method may involve obtaining a plurality of annotated training images including cargo, each of the training images being associated with an annotation indicating a type of the cargo in the training image, and training the image retrieval system by applying a deep learning algorithm to the obtained annotated training images. The training may involve applying, to the annotated training images, a feature extraction convolutional neural network, and applying an aggregated generalized mean pooling layer associated with image spatial information.

In some examples, it is disclosed a method for generating an image retrieval system configured to rank a plurality of images of cargo from a dataset of images, in response to a query corresponding to an image of cargo of interest generated using penetrating radiation. The method may involve obtaining a plurality of annotated training images including cargo, each of the training images being associated with an annotation indicating a type of the cargo in the training image, and training the image retrieval system by applying a deep learning algorithm to the obtained annotated training images. The training may involve applying, to the annotated training images, a feature extraction convolutional neural network, and applying an aggregated generalized mean pooling layer associated with image spatial information.

The present disclosure provides a logo picture processing method, apparatus, device and medium, and relates to technical field of image processing, and specifically to the technical field of artificial intelligence such as deep learning and computer vision. The logo picture processing method includes: obtaining a logo picture including: a current logo graph and current text information; performing text recognition on the logo picture to obtain the current text information; searching for a picture that matches both the current logo graph and the current text information, to obtain a matched picture. The present disclosure may improve the accuracy of the matched picture of the logo picture and thereby improve the logo picture recognition accuracy.

The present disclosure provides a logo picture processing method, apparatus, device and medium, and relates to technical field of image processing, and specifically to the technical field of artificial intelligence such as deep learning and computer vision. The logo picture processing method includes: obtaining a logo picture including: a current logo graph and current text information; performing text recognition on the logo picture to obtain the current text information; searching for a picture that matches both the current logo graph and the current text information, to obtain a matched picture. The present disclosure may improve the accuracy of the matched picture of the logo picture and thereby improve the logo picture recognition accuracy.

Computer-implemented methods and systems are provided for generating encoded representations for multiple magnifications of one or more query images. An example method for involves operating a processor to obtain a query image and identify a set of anchor points within the query image. The processor is operable to generate a plurality of sub-images for a plurality of magnification levels for each anchor point. Each sub-image includes the anchor point and corresponds to a magnification level of the plurality of magnification levels. The processor is operable to, for each magnification level, apply an artificial neural network model to a group of sub-images having that magnification level to extract a feature vector representative of image characteristics of the query image at that magnification level; and to generate an encoded representation for multiple magnifications of the query image based on the feature vectors extracted for the plurality of magnification levels.

Computer-implemented methods and systems are provided for generating encoded representations for multiple magnifications of one or more query images. An example method for involves operating a processor to obtain a query image and identify a set of anchor points within the query image. The processor is operable to generate a plurality of sub-images for a plurality of magnification levels for each anchor point. Each sub-image includes the anchor point and corresponds to a magnification level of the plurality of magnification levels. The processor is operable to, for each magnification level, apply an artificial neural network model to a group of sub-images having that magnification level to extract a feature vector representative of image characteristics of the query image at that magnification level; and to generate an encoded representation for multiple magnifications of the query image based on the feature vectors extracted for the plurality of magnification levels.

Technologies are described for retrieving documents using image representations in the documents and is based on intra-image features. The identification of elements within an image representation can allow for deeper understanding of the image representation and for better relating image representations based on their intra-image features. The intra-image features present in image representations can be used in searches. Search results can further be reranked to improve search results. For example, reranking can allow search results to conform to intra-image dominant image features.

Technologies are described for retrieving documents using image representations in the documents and is based on intra-image features. The identification of elements within an image representation can allow for deeper understanding of the image representation and for better relating image representations based on their intra-image features. The intra-image features present in image representations can be used in searches. Search results can further be reranked to improve search results. For example, reranking can allow search results to conform to intra-image dominant image features.

The present disclosure provides for systems and methods for generating an image of a web resource to detect a modification of the web resource. An exemplary method includes selecting one or more objects of the web resource based on one or more object attributes; identifying a plurality of tokens for each selected object based on contents of the selected object; calculating a hash signature for each selected object of the web resource using the identified plurality of tokens; identifying potentially malicious calls within the identified plurality of tokens; generating an image of the web resource based on the plurality of hash signatures and based on the identified potentially malicious calls, wherein the image of the web resource comprises a vector representation of the contents of the web resource; and detecting whether the web resource is modified based on the image of the web resource.

The present disclosure provides for systems and methods for generating an image of a web resource to detect a modification of the web resource. An exemplary method includes selecting one or more objects of the web resource based on one or more object attributes; identifying a plurality of tokens for each selected object based on contents of the selected object; calculating a hash signature for each selected object of the web resource using the identified plurality of tokens; identifying potentially malicious calls within the identified plurality of tokens; generating an image of the web resource based on the plurality of hash signatures and based on the identified potentially malicious calls, wherein the image of the web resource comprises a vector representation of the contents of the web resource; and detecting whether the web resource is modified based on the image of the web resource.