Universal Object Recognition via Siamese Network Feature Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional object recognition techniques are limited by the availability of training data and are not effective in recognizing arbitrary objects without additional training or data collection, especially in large-scale logo recognition systems.
Innovation Solution
The implementation of an open-set recognition system using a universal detector and verifier, which employs channel-wise max pooling and spatial verification networks to identify and match objects in images, allowing for the recognition of arbitrary objects without additional training data, utilizing a Siamese network structure and feature correlation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object recognition techniques are used, then recognition accuracy is limited by available training data, but system complexity and data collection requirements increase
Solution Approach 1:
The patent implements a universal object recognition system that can recognize any object class without requiring class-specific training data. The Siamese network architecture with channel-wise max pooling creates a universal feature extraction mechanism that works across all object types, eliminating the need for separate training processes for each object class while maintaining high recognition accuracy.
Solution Approach 2:
The system enables self-verification through the spatial verification network that operates independently to confirm object matches. The verifier network automatically validates recognition results without requiring external verification processes or additional human intervention, making the system self-sufficient while improving reliability.
2Measurement precision
If conventional recognition systems are trained on specific object classes, then recognition performance improves for those classes, but adaptability to new object classes deteriorates
Solution Approach 1:
The patent creates a universal recognition system where the Siamese network architecture extracts features that are applicable to any object class. The channel-wise max pooling operation generates class-agnostic feature representations that can be applied to recognize new object types without retraining, thus achieving both high performance and broad adaptability.
Solution Approach 2:
The system performs preliminary feature extraction and verification setup in advance through the trained Siamese network and spatial verification network. This preliminary action creates a flexible framework that can quickly adapt to new object classes by simply providing reference images, without requiring time-consuming retraining processes.
3Adaptability or versatility
If large-scale object recognition is implemented, then recognition coverage increases, but computational resources and processing time increase
Solution Approach 1:
The patent extracts only the most discriminative features through channel-wise max pooling operations in the Siamese network. By taking out and focusing on the most relevant feature channels rather than processing all features equally, the system achieves accurate large-scale object recognition while reducing computational overhead and processing time.
Solution Approach 2:
The spatial verification network performs a focused verification on specific spatial regions and key features rather than exhaustive comparison of all possible features. This partial action approach maintains high recognition coverage across large-scale object classes while significantly reducing the computational resources and time required for processing.
Data Source
AI summary
Large scale instance recognition is provided that can take advantage of channel-wise pooling. A received query image is processed to extract a set of features that can be used to generate a set of region proposals. The proposed regions of image data are processed using a trained classifier to classify the regions as object or non-object regions. Extracted features for the object regions are processed using feature correlation against extracted features for a set of object images, each representing a classified object. Matching tensors generated from the comparison are processed using a spatial verification network to determine match scores for the various object images with respect to a specific object region. The match scores are used to determine which objects, or types of objects, are represented in the query image. Information or content associated with the matching objects can be provided as part of a response.