4 results about "Automatic image annotation" patented technology

This application provides an automatic image annotation method and apparatus. The method includes acquiring an image to be annotated; identifying a first position of a target feature in the image by performing target feature detection on the image to be annotated; determining a bounding box corresponding to the target feature at the first position according to a preset annotation rule; determining an annotation strategy based on the distribution type of the target feature in the image to be annotated; selecting a target bounding box from the bounding boxes determined according to the preset annotation rule according to the annotation strategy; and annotating the target feature in the image to be annotated using the target bounding box. This method can accurately and automatically annotate target features in an image, with low cost, high speed, and high accuracy.

This application discloses an automatic image annotation method, apparatus, device, and storage medium. The method includes: annotating an original screenshot and saving the annotation information and image resolution of the original screenshot, the annotation information including annotation coordinate positions; recognizing the text information annotated in the original screenshot, the text information including text content and text coordinate positions; obtaining the image resolution of a new screenshot and recognizing the text content of the new screenshot to obtain the text information of the new screenshot; calculating a scaling ratio based on the image resolutions of the original and new screenshots, and obtaining the number of occurrences of the text content corresponding to the annotation coordinate positions in the original screenshot in the new screenshot by comparing the text content in the original screenshot with the text content in the new screenshot; obtaining the annotation information of the new screenshot based on the number of occurrences and the scaling ratio, and annotating the new screenshot based on the annotation information of the new screenshot. This improves the technical problem of low annotation efficiency in existing technologies that rely on manual image annotation.

The invention relates to the field of computer vision and machine learning, in particular to an image annotation method based on region segmentation and FAISS optimization lookup, and the method comprises the steps: carrying out the adaptive segmentation of a to-be-annotated image through OpenCV, carrying out the scanning of connected region marks twice, decomposing the image into a plurality of semantic independent regions, and carrying out the lookup of each region; constructing a two-stage FAISS index mechanism, introducing a ConvNeXt classification result as a prior constraint, and preferentially carrying out one-time retrieval in a same-class sample range; a dynamic clustering strategy is adopted, the number of clustering centers is adjusted in a self-adaptive mode according to the data scale, and a long-tail large cluster is dynamically split; and taking the distance between the similar image and the query image as a weight, carrying out weighted voting on the retrieved labels, fusing ConvNeXt classification confidence to dynamically adjust the retrieval weight, obtaining high-confidence labels of each region, and combining to form a whole image labeling result. According to the method, a region-level independent retrieval and dynamic clustering scattering mechanism is introduced, so that multi-target labeling is more sensitive and clearer; and a ConvNeXt classification result is utilized to limit a search space, so that cross-class missing check is avoided, and efficient and accurate automatic image annotation is realized.

This invention discloses an automatic image annotation method based on concept embedding search and active learning. The method includes the following steps: constructing an initial labeled sample set, and introducing a learnable embedding E based on the original concept embedding TEnc(T), forming a modified concept embedding TEnc(T)+E; performing M2C optimization on the learnable embedding E to obtain an updated E. t ; Utilizing the updated E t The concept embedding TEnc(T) is superimposed onto the original concept embedding to drive the SAM3 model to automatically generate concept-driven prediction segmentation masks for the remaining samples; a final mixed uncertainty score is calculated to comprehensively evaluate the difficulty of the samples; the prediction results are sorted, and the top-ranked samples are manually checked and corrected; this is fed back to step S2), driving the corrected concept embedding TEnc(T)+E to enter the next round of continuous search and update. This invention greatly reduces the cost of manual annotation while significantly improving the accuracy and robustness of image segmentation.