Object Part Location Prediction via Similarity Learning
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Solution Overview
Problem
Current data-driven detection methods for object part location are inefficient and inaccurate due to the lack of global consistency between parts, reliance on a sliding window approach, and limited contextual leverage, leading to high computational costs and suboptimal performance.
Innovation Solution
An extended data-driven detection method using a similarity learning algorithm to define a relevance value between part configurations, enabling the prediction of object part locations by measuring similarity between new images and training images, while accounting for global consistency and handling visible and non-visible parts, allowing for a single query independent of the number of parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sliding window approach is used for each part with DDD, then object part locations can be detected, but computational cost increases significantly
Solution Approach 1:
The patent segments the detection task by introducing part-specific templates that divide the image into multiple regions of interest corresponding to different object parts. Each template focuses on detecting a specific part location, allowing the system to process only relevant regions rather than evaluating all sub-regions globally. This segmentation reduces computational complexity while maintaining detection accuracy for multiple parts.
2Measurement precision
If DDD evaluates a large set of sub-regions, then comprehensive coverage is achieved, but accuracy decreases due to lack of contextual leverage
Solution Approach 1:
The patent applies local quality by using part-specific templates that are tailored to detect particular object parts in specific regions. Each template is designed with local characteristics relevant to its target part, allowing the detection system to focus computational resources on evaluating only the necessary sub-regions with appropriate contextual information, rather than uniformly processing all image regions.
3Reliability
If a high-level consistency model is employed to ensure part combination feasibility, then global consistency is achieved, but detection efficiency decreases
Solution Approach 1:
The patent performs preliminary action by pre-defining part-specific templates and their associated regions of interest before the actual detection process. These templates encode prior knowledge about part locations and configurations, allowing the system to directly evaluate relevant regions without requiring post-processing consistency checks. The preliminary setup ensures that detected part combinations are inherently feasible, eliminating the need for additional high-level consistency modeling during detection.
Data Source
AI summary
Methods and systems for detecting an object part location based on an extended date-driven detection. A specific relevance value between configurations of parts with respect to a set of training images annotated with annotating objects can be defined. A similarity learning algorithm can be applied with respect to the parts to obtain a similarity function based on the similarity between the part configurations. The similarity learning algorithm receives a set of positive pair having similar part configuration and a negative pair having different configuration and returns the similarity function that tends to assign a high score to new positive pairs and a low score to negative pairs. A similarity between a new image and the training images can be measured utilizing the learned similarity function to obtain a neighbor image and a visible and/or non-visible part location with respect to the image can be predicted based on the neighbor image.