A system and method for performing image scrolling are disclosed. In one embodiment, a system for image scrolling organizes each set of related images as a series object. The system writes selected images from one of the series objects, from the image cache to the frame buffer, for image scrolling on a display. A garbage collection module performs garbage collection in the image cache. The garbage collection module operates on memory space where a series object is released or can be moved, for reclaiming memory. The image scrolling is smoother than if the garbage collection module were to track and operate on each image as an object.

A system for selecting an image to accompany text from a user in connection with a social media post. The system is capable of receiving text from the user, identifying one or more search terms based on the text, identifying candidate images from images in one or more image databases using the search terms, presenting one or more candidate images to the user, receiving from the user a selected image from the one or more candidate images, generating the social media post comprising the selected image and the user-submitted text, and transmitting the social media post for display.

A method for automatically naming photos based on a mobile terminal, a system, and a mobile terminal are proposed. The method includes presetting a photo naming rule and storing the photo naming rule in the mobile terminal; updating, in real-time, calendar information of a naming resource provided in the mobile terminal; and searching for the naming resource corresponding to a current time from the calendar information when a new photo is detected to be stored, automatically naming the new photo according to the preset naming rule, and storing the named photo in a specific category.

Provided is a method of image searching based on artificial intelligence (AI), the method including acquiring retrieved information, which includes at least one of a retrieved image and an image address, and a user query on the basis of a search result of an image search engine, detecting a keyword-category combination on the basis of a type of the acquired user query, determining whether cache data that matches the detected keyword-category combination exists, generating, in response to absence of the cache data that matches the keyword-category combination, an object-category combination through an AI technology based object detection on the acquired retrieved information.

Provided is a method of image searching based on artificial intelligence (AI), the method including acquiring retrieved information, which includes at least one of a retrieved image and an image address, and a user query on the basis of a search result of an image search engine, detecting a keyword-category combination on the basis of a type of the acquired user query, determining whether cache data that matches the detected keyword-category combination exists, generating, in response to absence of the cache data that matches the keyword-category combination, an object-category combination through an AI technology based object detection on the acquired retrieved information.

Managing an image storage space is provided. A number of processor units identifies a benchmark image in a similar images group. A number of other images not identified in the similar images group as the benchmark image is a set of similar images. The number of processor units creates an image mapping tree. The image mapping tree has a root block for the benchmark image and blocks arranged in a set of layers below the root block based on the set of similar images; the blocks represent portions of the benchmark image; and a plurality of lower blocks in the blocks in a lower layer correspond to subdivisions in an upper block in the blocks in an upper layer. The number of processor units stores a set of selected blocks in the set of similar images that have differences from a set of corresponding blocks in image mapping tree for the benchmark image. The number of processor units store metadata for the set of selected blocks that describes set of paths in the image mapping tree from the set of corresponding blocks in the image mapping tree to the root block.

Managing an image storage space is provided. A number of processor units identifies a benchmark image in a similar images group. A number of other images not identified in the similar images group as the benchmark image is a set of similar images. The number of processor units creates an image mapping tree. The image mapping tree has a root block for the benchmark image and blocks arranged in a set of layers below the root block based on the set of similar images; the blocks represent portions of the benchmark image; and a plurality of lower blocks in the blocks in a lower layer correspond to subdivisions in an upper block in the blocks in an upper layer. The number of processor units stores a set of selected blocks in the set of similar images that have differences from a set of corresponding blocks in image mapping tree for the benchmark image. The number of processor units store metadata for the set of selected blocks that describes set of paths in the image mapping tree from the set of corresponding blocks in the image mapping tree to the root block.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Methods and systems for rending medical images within a web browser application. The web browser application retrieves a worklist and automatically determines an image study from the worklist to be cached. The web browser application retrieves at least one medical image included in the image study. The web browser application creates a web database for storing the at least one medical image within the browser application. When a user selects a medical image for display within the web browser, the web browser application determines whether the medical image is stored in the web database. When the medical image is stored in the web database, the web browser application retrieves the medical image from the web database. When the medical image is not stored in the web database, the web browser application retrieves the medical image from a remote image repository.