Abstract: A method for providing a style analysis model according to an embodiment of the present invention may comprise the operations of: acquiring a document written by a first user from an SNS used by the first user of an interior decoration service; determining a first text included in the document; determining a predetermined number of second texts in the first text of the document; generating a first feature vector configured on the basis of a frequency by which each of the second texts is included in the document; determining the style of the first user by inputting the first feature vector into a machine learning-based neural network model in which a correlation is pre-learned to derive a class specifying an interior decoration style for a predetermined feature vector; and recommending object information to which metadata of the style is mapped, on the basis of the determined style, when the first user requests information on a predetermined object provided by the interior decoration service.

Abstract: A method for providing a style analysis model according to one embodiment of the present invention may comprise: obtaining a training document including text data written by a user and determining a first text included in the training document; determining a predetermined number of second texts from among the first text of the training document; generating a first feature vector configured on the basis of the number of times each second text is included in training documents written by each user; generating, for each class, a second feature vector on the basis of the number of times each style-specific text is included in all the obtained training documents; labeling the first feature vector with a class of a second feature vector that is most similar to the first feature vector; and generating and training a machine learning-based neural network model that derives a correlation between the first feature vector and the class labeled in the first feature vector.

Abstract: According to one embodiment of the present invention, a symbol analysis device included in a facility floor plan can perform the operations of: acquiring a plurality of facility floor plans; detecting a rectangle included in each of the plurality of facility floor plans and an arc connected to the rectangle; specifying a window area and a door area on the basis of the rectangle and the arc; labeling pixels of the specified window area as the class of a window, and labeling pixels of the specified door area as the class of a door; and inputting the plurality of facility floor plans and data labeled in pixel units into a neural network model designed on the basis of a predetermined image segmentation algorithm, so as to learn the weight of the neural network model that derives the correlation between positions of the labeled pixels and the classes of windows and doors included in the plurality of facility floor plans, and thus a neural network model that determines the position and the class of windows and door

Abstract: A product recommendation device based on image database analysis, according to one embodiment of the present invention, can perform the operations of: acquiring, through a processor, an image database including an image file for products arranged in a predetermined space; extracting metadata that specifies space use, types of objects, space style, and color-suitable color arrangement, which are included in the image file, so as to map the metadata to the image file or product information about the products; determining the category of at least any one from among the space use, types of objects, space style, and color arrangement; and searching the image database for the image file or the product information mapped to the metadata corresponding to the determined category, so as to recommend same.

Abstract: Disclosed is a data augmentation-based preference analysis model learning apparatus including one or more processors, wherein the operation performed by the processor includes acquiring a plurality of space images and labeling a class specifying style information corresponding to each of the plurality of space images or acquiring the plurality of space images to which the class is labeled and generating learning data, generating a second space image by changing pixel information included in a first space image within a predetermined range among the plurality of space images and augmenting the learning data, and labeling a class labeled to the first space image, to the second space image.

Abstract: Disclosed is a data augmentation-based object analysis model learning apparatus including one or more processors, wherein the operation performed by the processor includes acquiring a first space image including a first object image and generating a second space image by changing pixel information included in the first space image, specifying a bounding box in a region including the first object image in the first space image and labeling a first class specifying the first object image in the bounding box, and primarily learning a weight of a model designed based on a predetermined object detection algorithm, for deriving a correlation between the first object image in the bounding box and the first class, by inputting the first space image to the model, specifying an object image included in a space image based on the correlation.

Abstract: Disclosed is a data augmentation-based space analysis model learning apparatus including one or more processors, wherein the operation performed by the processor includes acquiring a plurality of space images and labeling a class specifying space information corresponding to each of the plurality of space images or acquiring the plurality of space images to which the class is labeled and generating learning data, generating a second space image by changing some or all of pixel information included in a first space image among the plurality of space images and augmenting the learning data, labeling a class labeled to the first space image, to the second space image, and learning a weight of a model designed based on a predetermined image classification algorithm, for deriving a correlation between a space image included in the learning data and a class labeled to each of the space images.