An image searching apparatus includes: a processor; and a memory, wherein the processor is configured to attach, to an image with a first correct label attached thereto, a second correct label, the first correct label being a correct label attached to each image included in an image dataset for training for use in supervised training, the second correct label being a correct label based on a degree of similarity from a predetermined standpoint; execute main training processing to train a classifier by using the images and one of the first correct label and the second correct label, fine-tune a training state of the classifier; trained by the main training processing, by using the images and the other one of the first correct label and the second correct label; and search, by using the classifier that is fine-tuned, for images similar to a query image.

A method for retrieving at least one search image matching a query image commences by first extracting a set of search images. The query image is encoded into a query image feature vector and the search images are encoded into search image feature vectors using an optimized encoding process that makes use of learned encoding parameters. The Euclidean distances between the query image feature vector and the search image feature vectors are then computed. The search images are ranked based on the computed distances; and at least one highest-ranked search image is retrieved.

Methods, apparatuses, and embodiments related to analyzing the content of digital images. A computer extracts multiple sets of visual features, which can be keypoints, based on an image of a selected object. Each of the multiple sets of visual features is extracted by a different visual feature extractor. The computer further extracts a visual word count vector based on the image of the selected object. An image query is executed based on the extracted visual features and the extracted visual word count vector to identify one or more candidate template objects of which the selected object may be an instance. When multiple candidate template objects are identified, a matching algorithm compares the selected object with the candidate template objects to determine a particular candidate template of which the selected object is an instance.

One or more processor can automatically identify, structure and retrieve spatial and/or temporal sequences of digital media content according to semantic specification. Digital media content can be received and information from digital media content can be extracted. Based on the information, a knowledge graph can be constructed or structured to include at least one of spatial and temporal representation of the digital media content. A search query can be received associated with the digital media content. Based on traversing the knowledge graph structure according to at least one of spatial and temporal criterion mapped from the search query, new digital media content can be composed which meets the search query.

Techniques are provided that include obtaining a vocabulary including a set of content indices that reference corresponding cells in a descriptor space based on an input set of descriptors. A plurality of local features of an image are identified based on the vocabulary, the local features being represented by a plurality of local descriptors. An associated visual word in the vocabulary is determined for each of the plurality of local descriptors. A plurality of global signatures for the image are generated based on the associated visual words, wherein some of the plurality of global signatures are generated using local descriptors corresponding to different cropped versions of the image, two or more of the different cropped versions of the image being centered at a same pixel location of the image, and an image recognition search is facilitated using the plurality of global signatures to search a document image dataset.

One example method involves operations for receiving a query that includes a keyword. The search query is associated with a user profile. Operations further include recommendation matrix that includes a set of images based on (a) an area of interest determined from the search query and the user profile and (b) content tags associated with the images. In addition, operations include calculating a recommendation score for a candidate image included in the recommendation matrix. The recommendation score includes a weighted average of row vectors of the recommendation matrix. Further, operations involve including the candidate image in a search result for the search query based on the recommendation score. Additionally, operations include generating, for display, a search result that includes the candidate image.

A method for training a machine learning model included generating training data for the machine learning model. Generating the training data includes generating first training input that includes candidate text portions of one or more electronic documents and generating a first target output for the first training input. The first target output identifies a formatting type for each of the candidate text portions. The training data is provided to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output.

Methods and systems are presented for extracting categorizable information from an image using a graph that models data within the image. Upon receiving an image, a data extraction system identifies characters in the image. The data extraction system then generates bounding boxes that enclose adjacent characters that are related to each other in the image. The data extraction system also creates connections between the bounding boxes based on locations of the bounding boxes. A graph is generated based on the bounding boxes and the connections such that the graph can accurately represent the data in the image. The graph is provided to a graph neural network that is configured to analyze the graph and produce an output. The data extraction system may categorize the data in the image based on the output.

A detection apparatus to extract features from an image; determine the number of candidate regions of the object in the image based on the extracted features, wherein the determined number of the candidate regions is decided by a position and shape of the candidate regions; and to detect the object from the image based on at least the extracted features and the determined number, position and shape of the candidate regions.

One example method involves operations for receiving a query that includes a keyword. The search query is associated with a user profile. Operations further include a recommendation matrix that includes a set of images based on (a) an area of interest determined from the search query and the user profile and (b) content tags associated with the images. In addition, operations include calculating a recommendation score for a candidate image included in the recommendation matrix. The recommendation score includes a weighted average of row vectors of the recommendation matrix. Further, operations involve including the candidate image in a search result for the search query based on the recommendation score. Additionally, operations include generating, for display, a search result that includes the candidate image.