6results about How to "Reduce misclassification" patented technology

This invention relates to the field of intelligent surveillance technology, and more particularly to an image data retrieval method and system based on surveillance big data. The method includes: extracting the visibility and color features of pedestrian key points from surveillance trajectory segments; calculating viewpoint similarity using the cosine similarity of visibility vectors combined with the ratio of frequency standard deviations; selecting comparable key points and weighting them by the minimum visible frequency; calculating appearance similarity by combining color differences; introducing time constraint parameters to optimize the initial clustering distance to obtain a significant clustering distance; clustering matching for the same pedestrian trajectory; and finally establishing a retrieval index to complete image data retrieval. This invention improves pedestrian information retrieval performance by extracting key point visibility and color information, fusing optimized viewpoint and appearance similarity to construct an initial clustering distance, and optimizing with time constraint parameters to avoid interference from multi-camera scenes.

This invention discloses a multi-view few-sample Android malware classification method based on optimal guidance matching, belonging to the field of information security technology. The invention includes constructing multi-view grayscale images, training a backbone network, optimal guidance matching classification, and dynamic fusion of multiple views. First, the method extracts permissions, APIs, components, and intent features of the Android application to construct multi-view grayscale images. Then, it trains a backbone network that integrates attention mechanisms and self-supervised rotation prediction to extract discriminative features with geometric structure awareness. Valid guidance samples are identified through optimal guidance matching, and category similarity scores are calculated. Finally, adaptive weights are generated based on view confidence, and the multi-view scores are dynamically fused to complete the classification. This invention improves the accuracy and robustness of malware family classification in few-sample scenarios, reduces noise interference, and is suitable for rapid and accurate identification of malware.

This invention relates to a remote sensing extraction method for pear orchard areas based on the Re-UNet model, which overcomes the shortcomings of inaccurate classification results and low efficiency in pear orchard area extraction from remote sensing images compared with existing technologies. The invention includes the following steps: acquiring a remote sensing image dataset; constructing the Re-UNet pear orchard area extraction model; training the Re-UNet pear orchard area extraction model; acquiring and preprocessing the remote sensing images of the pear orchard areas to be segmented; and obtaining the remote sensing extraction results for the pear orchard areas. Based on the UNet semantic segmentation model, this invention solves the overfitting problem that easily occurs in small datasets. It also incorporates spatial and channel attention mechanisms and a residual module, further enhancing the feature transfer and cumulative integration characteristics of pear orchard areas in high-resolution remote sensing images, effectively reducing the "salt and pepper" phenomenon and misclassification, and improving the overall segmentation accuracy.

The application relates to the technical field of animation design material management, in particular to an animation design material library management system based on big data, which comprises a material collection module, a preprocessing standardization module, an intelligent clustering archiving module, a network construction module, an intention recommendation module and a dynamic optimization module. The system collects multi-source heterogeneous animation original data, carries out standardization processing, and automatically classifies and archives the data according to visual and semantic correlation degrees by using an improved clustering algorithm. On this basis, a multi-level semantic correlation network containing derivation, co-occurrence and style subordination relations is constructed. The network supports an intelligent recommendation process to respond to user creation intention description and output an ordered list of associated materials. The system also dynamically optimizes clustering parameters and network connection strength by analyzing user usage behavior. The application realizes deep semantic organization and intelligent recommendation of materials, and improves the management and utilization efficiency of animation materials.

The invention discloses a defect detection method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a first image; performing defect detection on the first image through a first model to obtain a first result; a first loss function is configured in the first model; the first loss function can instruct the first model to distinguish foreground feature information and background feature information of the first image. According to the method, the defect detection is performed on the first image through the first model, and the first loss function is configured in the first model, so that the foreground feature and the background feature of the first image can be aligned, and the feature consistency can be ensured, and therefore, the use of the first model can realize the automatic detection of the metal oxidation surface, and the detection efficiency is improved. And the defect identification precision of complex textures and edge areas on the metal oxidation surface can also be improved.

This application provides a method and system for segmenting hard exudates in diabetic retinopathy, relating to the field of image segmentation technology. The method involves determining the image complexity of a color fundus image; segmenting the color fundus image into multiple exudation regions of hard exudates based on the distribution characteristics of hard exudates in the diabetic retina; determining the edge contrast of each exudation region; determining the variability index of diabetic retinopathy based on all edge contrasts and image complexity; determining the convolution fusion boundary of all exudation regions based on the variability index and the boundary characteristics of each exudation region; and performing convolution fusion segmentation on all exudation regions of hard exudates based on the convolution fusion boundary to obtain multiple segmented regions of hard exudates in the color fundus image. This application can achieve convolution fusion segmentation of hard exudates in diabetic retinopathy, thereby improving the segmentation accuracy of hard exudates in diabetic retinopathy.