Abstract: Embodiments of the disclosure disclose a store system, and a method and an apparatus for processing information of clothing items for try-on. The store system comprises: a first device, deployed in a back warehouse area of a store and configured to obtain corresponding relationship between a user and information of one or more clothing items for try-on; and provide preparation prompt information based on the corresponding relationship to prepare the one or more clothing items for the user; and a fitting room, the fitting room comprising a first door and a second door, the first door configured for the user to enter and exit the fitting room, and the second door configured for delivering the one or more clothing items for try-on corresponding to the user from the back warehouse area to the fitting room.

Abstract: Embodiments of the present disclosure provide methods and devices for providing object coordinating information. The method comprises: submitting an object coordinating request to a server, receiving an object coordinating scheme returned by the server. The object coordinating scheme comprises data object information for a plurality of data objects. The plurality of data objects include a first data object that is a primary coordinating object and at least one second data object configured to coordinate with the first data object. The method can also include displaying the object coordinating scheme.

Abstract: Image processing includes dividing the plurality of images into a plurality of groups wherein images in the same group share the same main color; extracting a color feature vector (CFV) of each image in the plurality of groups; subdividing images in each of the plurality of groups into a plurality of subgroups using a clustering technique according to a distance between the CFVs of the images in the group to establish an image signature tree; searching among the plurality of subgroups for a result-subgroup having the same main color as the main color of a given image and containing an image whose CFV has the shortest distance from the CFV of the given image; comparing the CFV of the given image with the CFVs in the result group; and identifying a near-duplicate image from the result group that meets a preset near-duplicate image determining condition.

Abstract: Image processing includes dividing the plurality of images into a plurality of groups wherein images in the same group share the same main color; extracting a color feature vector (CFV) of each image in the plurality of groups; subdividing images in each of the plurality of groups into a plurality of subgroups using a clustering technique according to a distance between the CFVs of the images in the group to establish an image signature tree; searching among the plurality of subgroups for a result-subgroup having the same main color as the main color of a given image and containing an image whose CFV has the shortest distance from the CFV of the given image; comparing the CFV of the given image with the CFVs in the result group; and identifying a near-duplicate image from the result group that meets a preset near-duplicate image determining condition.

Abstract: Image processing includes receiving a plurality of images to be grouped; dividing the plurality of images into a plurality of groups wherein the images in the same group share the same main color; receiving a given image; searching among the plurality of groups for a result group having the same main color as the main color of the given image; extracting a plurality of image features of the given image and the images in the result-group; comparing the image features of the given image with the image features of each image in the result group; and identifying a near-duplicate image from the result group that meets a preset near-duplicate image determining condition.

Abstract: An image recognition technique includes obtaining color characteristic values of pixels in a source image; determining a main color of the source image based at least in part on the color characteristic values; and determining whether the source image is a non-product image based at least in part on the main color of the source image.

Abstract: Functionality is described for generating a vocabulary from a source dataset of image items or other non-textual items. The vocabulary serves as a tool for retrieving items from a target dataset in response to queries. The vocabulary has at least one characteristic that allows it to be used to retrieve items from multiple different target datasets. A target dataset can have a different size than the source dataset and/or a different type than the source dataset. The enabling characteristic may correspond to a size of the source dataset above a prescribed minimum number of items and/or a size of the vocabulary above a prescribed minimum number of words.

Abstract: An image recognition technique includes obtaining color characteristic values of pixels in a source image; determining a main color of the source image based at least in part on the color characteristic values; and determining whether the source image is a non-product image based at least in part on the main color of the source image.

Abstract: Image processing includes receiving a plurality of images to be grouped; dividing the plurality of images into a plurality of groups wherein the images in the same group share the same main color; receiving a given image; searching among the plurality of groups for a result group having the same main color as the main color of the given image; extracting a plurality of image features of the given image and the images in the result-group; comparing the image features of the given image with the image features of each image in the result group; and identifying a near-duplicate image from the result group that meets a preset near-duplicate image determining condition.

Abstract: A system for automatically classifying an object of a target image is provided. A classification system provides a collection of classified images along with a classification of the dominant object of the image. The classification system attempts to classify the object of a target image based on similarity of the target image to the classified images. To classify a target image, the classification system identifies the classified images of the collection that are most similar to the target image based on similarity between salient points of the target image and the classified images. The classification system selects a classification associated with the classified images that are most similar to the target image as a classification for the object of the target image.

Abstract: Functionality is described for generating a vocabulary from a source dataset of image items or other non-textual items. The vocabulary serves as a tool for retrieving items from a target dataset in response to queries. The vocabulary has at least one characteristic that allows it to be used to retrieve items from multiple different target datasets. A target dataset can have a different size than the source dataset and/or a different type than the source dataset. The enabling characteristic may correspond to a size of the source dataset above a prescribed minimum number of items and/or a size of the vocabulary above a prescribed minimum number of words.

Abstract: A system for automatically classifying an object of a target image is provided. A classification system provides a collection of classified images along with a classification of the dominant object of the image. The classification system attempts to classify the object of a target image based on similarity of the target image to the classified images. To classify a target image, the classification system identifies the classified images of the collection that are most similar to the target image based on similarity between salient points of the target image and the classified images. The classification system selects a classification associated with the classified images that are most similar to the target image as a classification for the object of the target image.