Deep Cognitive Attribution Neural Network for Image Tagging
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Solution Overview
Problem
Conventional systems for digital content recommendation and image classification are inflexible, inefficient, and inaccurate, struggling to effectively analyze and identify attributes within digital images, leading to suboptimal search results and recommendations.
Innovation Solution
A deep cognitive attribution neural network is employed, utilizing a base neural network with interleaved dilated convolution blocks and inception modules for attribute localization, combined with attribute group classifiers to predict attributes and generate tags, enabling flexible and accurate identification of digital attributes within images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional classification models are used to identify attributes in digital images, then the system can provide digital recommendations and search results, but the system lacks flexibility and cannot easily scale to include different attributes without individual training of separate classifiers
Solution Approach 1:
The patent implements a universal attribute classification system using a single deep neural network that can classify multiple different attributes across various attribute groups simultaneously. The network architecture includes shared feature extraction layers followed by attribute-specific classification layers, allowing the system to handle diverse attributes (e.g., color, texture, shape, object properties) without requiring separate trained classifiers for each attribute type.
2Measurement precision
If conventional classification models with large numbers of parameters are used, then the system can process digital images, but excessive computation expense and processing resources are required for training and application
Solution Approach 1:
The patent segments the attribute classification task into distinct attribute groups (e.g., color attributes, texture attributes, shape attributes, object attributes), with each group handled by dedicated classification layers within the neural network. This segmentation allows the system to process only relevant attribute features for each classification task, reducing unnecessary computation while maintaining comprehensive attribute coverage.
Solution Approach 2:
The neural network employs dynamic feature extraction and adaptive pooling mechanisms that adjust the processing depth and breadth based on the input image characteristics and the specific attribute group being classified. This dynamic approach optimizes computational resources by processing only the necessary features at appropriate levels of detail.
3Productivity
If conventional systems identify similar digital images based on similarity metrics, then the system can provide search results, but the system fails to accurately generate tags or search responses that reflect desired features or attributes portrayed in the digital image
Solution Approach 1:
The patent implements feedback mechanisms where the neural network's attribute classification results are used to refine and filter search results. The system generates tags based on identified attributes and uses these tags to improve the relevance of search responses, creating a feedback loop that continuously enhances the accuracy of attribute-based search and recommendation functions.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating tags for an object portrayed in a digital image based on predicted attributes of the object. For example, the disclosed systems can utilize interleaved neural network layers of alternating inception layers and dilated convolution layers to generate a localization feature vector. Based on the localization feature vector, the disclosed systems can generate attribute localization feature embeddings, for example, using some pooling layer such as a global average pooling layer. The disclosed systems can then apply the attribute localization feature embeddings to corresponding attribute group classifiers to generate tags based on predicted attributes. In particular, attribute group classifiers can predict attributes as associated with a query image (e.g., based on a scoring comparison with other potential attributes of an attribute group). Based on the generated tags, the disclosed systems can respond to tag queries and search queries.