An image retrieval system with high retrieval accuracy is provided. The image retrieval system includes a database and a processing portion. The database has a function of storing a plurality of pieces of database image data, and a database tag is linked to each of the plurality of pieces of database image data. The processing portion has a function of obtaining database image feature value data representing a feature value of the database image data for each piece of the database image data. The processing portion has a function of obtaining query image feature value data representing a feature value of the query image data. The processing portion has a function of calculating first similarity of the database image data to the query image data for each piece of the database image data. The processing portion has a function of obtaining a query tag linked to the query image data using some of the database tags.

A system for home clothing and footwear products arrangement includes a shelter arrangement which comprises a plurality of shelter elements for storing and sheltering a plurality of dressing items, a control center which is a computing device, an electrical terminal, a position indication arrangement for indicating a position of a target dressing item for a user, and an identifier coupled to the shelter arrangement for establishing communication between the control center and the electrical terminal, so as to allow a user to quickly find his or her target clothing or footwear product.

System and methods for type ahead search amelioration based on image processing are provided. In embodiments, a method includes: capturing, by a computing device, image data based on images viewed by a user during a computing session; converting, by the computing device, the image data to text using image processing; and storing, by the computing device, the text in a temporary buffer of a type ahead search function, wherein the text constitutes image context data for use by the type ahead search function.

A localization and mapping method is for localizing and mapping a moving apparatus in a moving process. The localization and mapping method includes an image capturing step, a feature point extracting step, a flag object identifying step, and a localizing and mapping step. The image capturing step includes capturing an image frame at a time point of a plurality of time points in the moving process by a camera unit. The flag object identifying step includes identifying whether the image frame includes a flag object among a plurality of the feature points in accordance with a flag database. The flag database includes a plurality of dynamic objects, and the flag object is corresponding to one of the dynamic objects. The localizing and mapping step includes performing localization and mapping in accordance with the image frames captured and the flag object thereof in the moving process.

A front-end image acquisition component acquires photographs and/or videos of the exterior of a vehicle traveling on a path between a first predefined location and a second predefined location. In one aspect, an image acquisition system comprises a first camera support structure, a second camera support structure, an image processor, and a memory. Cameras are affixed to each of the camera support structures for acquiring image data of a subject vehicle. Image data is controlled by the image processor and the memory. In another aspect, an image acquisition system comprises a first camera tower, a second camera tower, a camera boom. Cameras are affixed to each of the camera towers. In another aspect, a method of identifying features of the exterior of a vehicle includes acquiring a series of images from a front-end component, accessing previously acquired images and associated metadata stored in a database, executing machine learning algorithms using the previously acquired information to identify at least one exterior vehicle feature, and executing machine learning algorithms using the presently acquired series of images to identify at least one exterior feature of the vehicle.

A front-end image acquisition component acquires photographs and/or videos of the exterior of a vehicle traveling on a path between a first predefined location and a second predefined location. In one aspect, an image acquisition system comprises a first camera support structure, a second camera support structure, an image processor, and a memory. Cameras are affixed to each of the camera support structures for acquiring image data of a subject vehicle. Image data is controlled by the image processor and the memory. In another aspect, an image acquisition system comprises a first camera tower, a second camera tower, a camera boom. Cameras are affixed to each of the camera towers. In another aspect, a method of identifying features of the exterior of a vehicle includes acquiring a series of images from a front-end component, accessing previously acquired images and associated metadata stored in a database, executing machine learning algorithms using the previously acquired information to identify at least one exterior vehicle feature, and executing machine learning algorithms using the presently acquired series of images to identify at least one exterior feature of the vehicle.

A computer implemented method to present digital images may include storing a digital image in a database and applying a digital image processing technique to the digital image to identify a region of interest of the digital image. The method may also include storing region data that identifies the region of interest of the digital image in the database and receiving a request for information associated with the digital image from a digital device. In response to the request, the method may include providing the digital image and the region data for transmission to the digital device, the digital device configured to adjust a cropping view of the digital image based on the region data to display the region of interest of the digital image.

A method for organizing a pattern comprises receiving an image of a pattern, processing the image to find text in the image, comparing the found text to data in a memory, identifying the data in the memory that corresponds to the found text, and presenting the found data and the data in the memory that corresponds to the found text to a user.

In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

The present disclosure provides systems and methods that use and/or generate image files according to a novel microvideo image format. For example, a microvideo can be a file that contains both a still image and a brief video. The microvideo can include multiple tracks, such as, for example, a separate video track, audio track, and/or one or more metadata tracks. As one example track, the microvideo can include a motion data track that stores motion data that can be used (e.g., at file runtime) to stabilize the video frames. A microvideo generation system included in an image capture device can determine a trimming of the video on-the-fly as the image capture device captures the microvideo.