Feature-Guided Network for Image Classification and Segmentation
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Solution Overview
Problem
Existing image classification and segmentation network models fail to fully utilize and enhance features, leading to unsatisfactory classification and segmentation accuracy due to insufficient feature guidance and reuse.
Innovation Solution
A feature-guided network approach is implemented, using a classification network with 3×3 convolutional layers, basic unit blocks with residual connections, and a fully-connected network for classification, and a segmentation network with an encoder-decoder structure and depthwise separable convolution for image segmentation, enhancing local features spatially and extracting global features for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification network models are used, then the model structure is simple, but the classification accuracy is low due to insufficient feature utilization
Solution Approach 1:
The network is divided into multiple basic unit blocks, each responsible for specific feature extraction tasks. Each block contains parallel convolutional layers with different kernels that process features at different levels, allowing systematic feature utilization without overwhelming complexity
Solution Approach 2:
Basic unit blocks are nested within the overall network structure, with each block containing nested convolutional layers and feature processing units. This nested architecture allows features to be processed at multiple levels of abstraction, improving accuracy while maintaining manageable complexity through modular design
2Measurement precision
If existing segmentation network models are used, then the model structure is relatively simple, but the segmentation accuracy is insufficient due to ignoring intensive feature extraction and full reuse of features
Solution Approach 1:
The network maintains continuous feature processing through the encoder-decoder structure, where features extracted in the encoder are continuously refined and reused in the decoder. Skip connections ensure that useful features are preserved and continuously utilized throughout the segmentation process, improving accuracy without requiring completely separate processing paths
Solution Approach 2:
Different parts of the network are assigned different functions: the encoder focuses on extracting local features through convolutional layers, while the decoder focuses on reconstructing the segmentation map. Basic unit blocks within the encoder use different kernel sizes to capture features at different local scales, improving segmentation accuracy through specialized local processing
3Extent of automation
If manual feature design is used in traditional machine learning, then the algorithm is easier to interpret, but it requires manual intervention and does not automatically extract features
Solution Approach 1:
The convolutional neural network performs self-service feature extraction through its layered architecture. Each basic unit block automatically learns and extracts features from the input data without human intervention, with features being progressively refined through multiple layers. The network serves itself by automatically adapting to the data distribution and learning optimal feature representations
Data Source
AI summary
The present invention provides an apparatus and method for image classification and segmentation based on a feature-guided network, a device, and a medium, and belongs to the technical field of deep learning. A feature-guided classification network and feature-guided segmentation network of the present invention include basic unit blocks. A local feature is enhanced and a global feature is extracted among the basic unit blocks. This resolves a problem that features are not fully utilized in existing image classification and image segmentation network models. In this way, a trained feature-guided classification network and feature-guided segmentation network have better effects and are more robust. The present invention selects the feature-guided classification network or the feature-guided segmentation network based on a requirement of an input image and outputs a corresponding category or segmented image, to resolve a problem that the existing classification or segmentation network model has an unsatisfactory classification or segmentation effect.