Hybrid Fisher Vector Neural Network for Image Classification
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Solution Overview
Problem
Existing image classification systems, such as CNNs, face challenges with image distortion due to resizing and lack of geometric invariance, and require large labeled datasets for training, making them computationally burdensome and less adaptable to changing image characteristics.
Innovation Solution
A hybrid BoP/NN pipeline that extracts local descriptors, encodes them using Fisher Vector encoding, and projects them into a lower dimensional space using PCA, followed by a neural network for classification, allowing for unsupervised feature extraction and training without labeled data, thus retaining geometric invariance and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CNN architectures are used for image classification, then classification accuracy is improved, but computational complexity and training data requirements increase significantly
Solution Approach 1:
The image processing task is segmented into two distinct stages: (1) unsupervised feature extraction that divides the image into patches and extracts local descriptors, and (2) supervised classification using a simpler linear classifier. This segmentation allows the complex feature extraction to be performed once unsupervised, while classification can be done efficiently with minimal computational resources.
Solution Approach 2:
The system performs preliminary unsupervised feature extraction and encoding before classification. By pre-computing the Fisher Vector encoding from local descriptors in an unsupervised manner, the system prepares the data in advance so that the subsequent classification step requires minimal computational effort and no labeled training data.
2Measurement precision
If image resizing and spatial registration are applied to compensate for differences in lighting, position, or orientation, then classification performance is improved, but image distortion increases
Solution Approach 1:
Instead of applying global transformations to the entire image, the system extracts local descriptors from individual patches distributed across the image. Each patch is processed independently, preserving local geometric characteristics while allowing the system to handle variations in lighting, position, and orientation through the aggregation of these local features into a Fisher Vector encoding.
3Measurement precision
If supervised learning with labeled training data is used to train image classifiers, then classification accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary unsupervised feature extraction and Fisher Vector encoding before classification. By pre-computing the feature encoding without requiring labeled data, the system prepares the data in advance so that the subsequent classification step requires minimal computational effort and no labeled training data.
Solution Approach 2:
The system extracts and separates the feature extraction and encoding operations from the classification training process. By taking out the unsupervised feature extraction as a distinct preliminary step, the system eliminates the need for computationally intensive supervised training of complex feature extractors, reducing training time and resource requirements.
Data Source
AI summary
In an image classification method, a feature vector representing an input image is generated by unsupervised operations including extracting local descriptors from patches distributed over the input image, and a classification value for the input image is generated by applying a neural network (NN) to the feature vector. Extracting the feature vector may include encoding the local descriptors extracted from each patch using a generative model, such as Fisher vector encoding, aggregating the encoded local descriptors to form a vector, projecting the vector into a space of lower dimensionality, for example using Principal Component Analysis (PCA), and normalizing the feature vector of lower dimensionality to produce the feature vector representing the input image. A set of mid-level features representing the input image may be generated as the output of an intermediate layer of the NN.