Fusing Bottom-Up Image Features with Top-Down Entity Classification
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Solution Overview
Problem
Existing convolutional neural network (CNN) systems for image and video scene classification do not effectively utilize entity information, limiting their generalizability to scenes with different entity relationships from the training set, and require feature selection by humans, which is not efficient.
Innovation Solution
A system that uses two parallel processing pipelines, one for entity classification and one for whole-image feature extraction, combining entity and visual feature spaces to generate a fused scene class, with classifiers trained on the combined features for accurate scene classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CNN systems use only whole image features for scene classification, then processing is simpler and faster, but entity information is not effectively utilized, limiting generalizability
Solution Approach 1:
The processing pipeline is segmented into two independent parallel pipelines: one for entity classification and one for whole-image feature extraction. This segmentation allows each pipeline to specialize in its specific task, improving overall system adaptability while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The two independent pipelines are merged through a fusion mechanism that combines entity features and whole-image features to generate a fused scene class. This merging enables the system to leverage both entity-level and image-level information, significantly improving generalizability to diverse scenes.
2Extent of automation
If traditional neural networks are used for scene classification, then feature selection can be done manually, but the system lacks automated feature learning capability and requires human intervention
Solution Approach 1:
The system implements self-service through automated feature learning where the neural networks automatically extract and learn discriminating features from the input images without requiring manual feature selection. The deep learning models self-adjust and optimize their feature representations during training, eliminating the need for human feature engineering while maintaining effective scene classification.
3Measurement precision
If independent entity recognition component is used with CNN, then entity detection is improved, but spatial relationship learning between entities and scenes is compromised
Solution Approach 1:
The system segments the processing into independent entity classification and whole-image feature extraction pipelines, allowing each to optimize for its specific function. The entity pipeline achieves high detection accuracy while the whole-image pipeline captures spatial relationships, and the two are later fused to achieve both goals simultaneously.
Solution Approach 2:
The system transitions from a single-dimensional processing approach to a multi-dimensional approach by extracting features at multiple levels (entity level and image level) and then fusing them. This dimensional expansion allows the system to capture both precise entity information and spatial relationships without compromising either aspect.
Data Source
AI summary
Described is a system and method for accurate image and/or video scene classification. More specifically, described is a system that makes use of a specialized convolutional-neural network (hereafter CNN) based technique for the fusion of bottom-up whole-image features and top-down entity classification. When the two parallel and independent processing paths are fused, the system provides an accurate classification of the scene as depicted in the image or video.