Abstract: A checkout apparatus for barcodeless merchandise, the checkout apparatus includes an image camera module and a control module coupled to the image camera module. The image camera module obtains a merchandise image includes the barcodeless merchandise. The control module receives the merchandise image and generates an output datum according to a result of a merchandise identification program after the control module performs the merchandise identification program on the merchandise image. At least one of the image camera module and the control module is coupled to the point of sales (POS) management system or is embedded in the POS management system, the output datum is readable for the POS management system.

Abstract: An image recognizing method adopted by a platform is disclosed. The method first receives multiple targets to be recognized at the platform, and inquiries a pre-established semantic tree by reference to the targets for determining if the recognition results of the multiple targets will cause confusion or not. If confusion is not foreseeable, the method obtains respectively a parent-classifier corresponding to each parent-category of each of the targets, and uses the parent-classifiers directly to perform a recognition action to the targets. Otherwise, the method obtains respectively multiple child-classifiers corresponding to multiple subcategories below each of the targets, and uses the multiple child-classifiers to perform such recognition action to the targets.

Abstract: A video dotting placement analysis method is disclosed, including: converting a content of the video into a plurality of descriptor lists, wherein each descriptor list is recorded with a time sequence and a plurality of raw descriptors; providing an advertisement category (ADC) model recorded with relationships among a plurality of advertisement categories and a plurality of descriptors; performing analysis on the ADC model and the plurality of descriptor lists to generate a plurality of ADC recommendation lists, wherein the plurality of ADC recommendation lists is recorded with category relevance confidences between each advertisement category and the video content corresponded to each time sequences; calculating predicted audience response (AR) values of each advertisement category; and analyzing one or multiple time sequences as a dotting placement of the video based on the plurality of ADC recommendation lists, the plurality of predicted AR values and a dotting model.

Abstract: A method for identifying extension messages of a video includes: providing a video; converting content of the video into a content list including a plurality of descriptor lists, each descriptor list recording a time interval and raw descriptors for describing a feature presented in the video at the time interval; providing a descriptor semantic model (DSM) including a plurality of node descriptors and a plurality of directed edges wherein each node descriptor corresponds to a predetermined feature, and the directed edges define relation strengths among the node descriptors; importing the raw descriptors of the descriptor list into the DSM to update the raw descriptors as refined descriptors and to obtain one or more inferred descriptors; and updating the descriptor lists based on the refined descriptors and the inferred descriptors. An identification system and storage media thereof are also provided.

Abstract: A computer-vision based automatic inserting and playing method for advertisement analyzes a video for obtaining multiple shots of the video, and obtaining semantic information related to targets appearing in each shot and low-level information related to vision of each shot. Next, the method matches the semantic information and the low-level information of each two adjacent shots with other semantic information and other low-level information of candidate advertisements, so as to determine one or more insertion time points of the video, and one or more inserting advertisements respectively related to each of the insertion time points. The matching of the semantic information ensures the inserting advertisement and a front shot and a rear shot of the insertion time point are all related with each other, and the matching of the low-level information enhances visual fluency in playing and switching among the front shot, the inserting advertisement and the rear shot.

Abstract: A computer-vision based automatic inserting and playing method for advertisement analyzes a video for obtaining multiple shots of the video, and obtaining semantic information related to targets appearing in each shot and low-level information related to vision of each shot. Next, the method matches the semantic information and the low-level information of each two adjacent shots with other semantic information and other low-level information of candidate advertisements, so as to determine one or more insertion time points of the video, and one or more inserting advertisements respectively related to each of the insertion time points. The matching of the semantic information ensures the inserting advertisement and a front shot and a rear shot of the insertion time point are all related with each other, and the matching of the low-level information enhances visual fluency in playing and switching among the front shot, the inserting advertisement and the rear shot.

Abstract: An image recognizing method adopted by a platform is disclosed. The method first receives multiple targets to be recognized at the platform, and inquiries a pre-established semantic tree by reference to the targets for determining if the recognition results of the multiple targets will cause confusion or not. If confusion is not foreseeable, the method obtains respectively a parent-classifier corresponding to each parent-category of each of the targets, and uses the parent-classifiers directly to perform a recognition action to the targets. Otherwise, the method obtains respectively multiple child-classifiers corresponding to multiple subcategories below each of the targets, and uses the multiple child-classifiers to perform such recognition action to the targets.

Abstract: A visible advertising system includes steps of: receiving a searching keyword; searching for videos according to the searching keyword in order to obtain multiple frames of the searched videos which include a target corresponding to the searching keyword; analyzing the multiple frames in order to filter more than one worth-to-advertise advertising points; selecting one of the advertising points for advertising an advertisement; setting a content of the advertisement and audience conditions for a target audience of the advertisement; bidding for the selected advertising point after being triggered; and assigning the advertisement to the selected advertising point once the bidding is wining.