Superpixel Pooling Layer for Efficient Image Classification
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Solution Overview
Problem
Existing deep learning algorithms face inefficiencies in image classification due to the large amount of data from high-quality images, particularly when classifying objects in units of pixels, leading to difficulties in grasping feature values of superpixels which are often smaller than grid sizes, resulting in multiple superpixels within a single grid.
Innovation Solution
An image processing apparatus and method that extracts superpixels from input images, utilizes a backbone network with multiple feature extracting layers to generate feature values for regions, and applies a superpixel pooling layer to calculate representative feature values for each superpixel, considering its size and overlapping regions, while also determining optimal superpixel parameters for improved classification efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If objects are classified in units of pixels, then classification granularity is improved, but data processing amount increases excessively
Solution Approach 1:
The patent merges multiple pixels into superpixels that represent objects or object parts. By grouping similar pixels together based on color, texture, and spatial proximity, the system reduces the number of classification units from individual pixels to superpixels, thereby decreasing data processing amount while maintaining classification granularity.
Solution Approach 2:
The patent segments the image into superpixels using algorithms like SLIC (Simple Linear Iterative Clustering), which divides the image into regions of similar characteristics. This segmentation approach creates meaningful units for classification that are larger than pixels but smaller than the entire image, balancing granularity and processing efficiency.
2Quantity of substance
If superpixels are formed by grouping similar pixels, then data processing amount is reduced, but feature value extraction becomes difficult when superpixel size is smaller than grid size
Solution Approach 1:
The patent introduces a new dimension by creating superpixel-level feature maps in addition to the original pixel-level feature maps from the backbone network. Instead of forcing superpixels to fit into fixed grid structures, the system generates features at multiple scales and resolutions, allowing superpixels of various sizes to be effectively represented.
Solution Approach 2:
The patent implements a nested structure where superpixel features are embedded within the overall image feature hierarchy. The superpixel pooling layer aggregates features from multiple sources (original image features, superpixel segmentation features) and nests them together to create comprehensive representations that work regardless of superpixel size relative to grid size.
3Device complexity
If multiple superpixels are included in one grid, then superpixel representation is simplified, but unique feature value for each superpixel cannot be grasped
Solution Approach 1:
The patent applies local quality by processing each superpixel individually through the superpixel pooling layer, which extracts and aggregates features specific to each superpixel region. This ensures that even when multiple superpixels fall within a single grid cell, each superpixel maintains its unique feature representation based on its local characteristics rather than being averaged with neighboring superpixels.
Solution Approach 2:
The superpixel pooling layer acts as an intermediary between the backbone network's grid-based features and the final classification layer. It mediates by aggregating features from multiple grids that correspond to a single superpixel, and by distributing superpixel-specific features back to appropriate grid locations, ensuring unique feature values are preserved and propagated through the network.
Data Source
AI summary
An image processing apparatus includes a superpixel extractor configured to extract a plurality of superpixels from an input original image, a backbone network including N feature extracting layers (here, N is a natural number of two or more) which divide the input original image into grids including a plurality of regions and generate an output value including a feature value for each of the divided regions, and a superpixel pooling layer configured to generate a superpixel feature value corresponding to each of the plurality of superpixels using a first output value to an Nth output value output from each of the N feature extracting layers.