Hierarchical Object Detection Framework for Visual Search
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Solution Overview
Problem
Current object detection systems face challenges in accurately identifying objects within images and videos due to issues like imaging noises, scale variations, occlusion, and appearance similarity with the background or other objects, which affect their robustness and accuracy in localization and category separation.
Innovation Solution
A deep learning-based object detection framework that predicts a hierarchical tree of categories instead of a single category, utilizing a hierarchical detection framework to analyze detection errors and model correlations between object categories, allowing for improved generalization to novel objects without the need for new labeled data or retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single category prediction is used in object detection, then the system complexity is low, but the accuracy in separating objects within categories from other objects deteriorates
Solution Approach 1:
The patent segments the object detection task into multiple hierarchical levels, where each level predicts a specific category or sub-category. This hierarchical segmentation allows the system to progressively refine object classification from broad categories to specific instances, improving separation accuracy between similar objects while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces a hierarchical dimension to the category prediction space, transforming the traditional single-level classification into a multi-level hierarchical structure. This dimensional expansion allows the system to capture fine-grained distinctions between objects by adding category depth, thereby improving detection precision without proportionally increasing overall system complexity.
2Measurement precision
If hierarchical category prediction is implemented, then the separation of objects within category is improved, but the computational complexity increases
Solution Approach 1:
The patent employs preliminary action by performing coarse-grained category predictions at earlier stages of the hierarchical process, which narrows down the search space for subsequent fine-grained predictions. This preliminary classification reduces the computational burden on later stages by eliminating irrelevant categories beforehand, thereby improving overall category separation accuracy while controlling computational power requirements.
Solution Approach 2:
The patent implements dynamic computation allocation within the hierarchical framework, where computational resources are adaptively distributed across different hierarchy levels based on the complexity and importance of each prediction stage. This dynamic approach allows the system to optimize computational power usage by focusing more resources on challenging discriminations while using fewer resources for easier categorizations.
3Reliability
If traditional object detection is used, then the system is simple to implement, but the robustness against imaging noises and occlusion deteriorates
Solution Approach 1:
The patent segments the detection process into multiple hierarchical stages, where each stage processes and refines detection results independently. This segmentation allows the system to progressively filter out imaging noises and handle occlusion effects at different levels of abstraction, improving detection robustness while maintaining relatively simple processing at each individual stage through modular design.
Data Source
AI summary
Systems that search databases of videos or images to identify similar products in a given video or image of a product are disclosed. The content of the given video is represented by a feature vector used to measure the given video's similarity to either a video or an image. When the system is deployed to recognize particular fashion items in videos, some such videos are taken in uncontrolled settings, and as a result, may have low resolution, poor contrast, minimal focus, motion blur, or low lighting. By recognizing and removing poor quality video frames from the image recognition pipeline, associating products across video frames to form tracklets of each product, and enriching the feature representation of each item for best retrieval result by fusing information from multiple video frames depicting the item, the system addresses the aforementioned shortcomings.