Appearance and Context Object Classification
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Solution Overview
Problem
Existing object classification methods in computer vision fail to effectively utilize structural information and contextual relationships within images, leading to high computational complexity and false positive errors, as they primarily rely on appearance information and simple geometric relations.
Innovation Solution
A method that uses a sequential application of an appearance classifier to select candidate windows and a context classifier to determine object identity and location, leveraging appearance and context features such as Histogram of Gradients (HoG) and Haar-like features, along with relative and global context features to reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a brute force search with appearance-based classification is applied to all locations and sizes, then object detection coverage is improved, but computational complexity increases significantly
Solution Approach 1:
The patent divides the image processing task into multiple stages: first extracting interest points and generating candidate windows, then applying appearance classification only to these candidates, and finally using context classification to further filter results. This segmentation allows comprehensive detection coverage while reducing computational complexity by avoiding brute force search across the entire image space.
Solution Approach 2:
The patent performs preliminary actions by first detecting interest points and generating candidate windows before applying classification. This preliminary filtering step reduces the search space significantly, allowing subsequent appearance and context classification to be applied only to relevant regions rather than the entire image.
2Productivity
If only appearance information from pixels in the window is used, then classification speed is improved, but structural information and contextual relationships are lost
Solution Approach 1:
The patent segments the classification process into two distinct stages: appearance classification based on pixel information within candidate windows, and context classification based on structural and contextual relationships. This segmentation allows the system to maintain classification speed through efficient appearance-based filtering while subsequently incorporating structural information to improve accuracy.
Solution Approach 2:
The patent transitions from two-dimensional appearance information within windows to a broader contextual dimension by analyzing relationships between multiple windows and objects in the scene. The context classification stage incorporates spatial relationships, object interactions, and scene-level information that go beyond local pixel patterns.
3Device complexity
If simple geometric relationships between object parts are used, then computational complexity is reduced, but detection accuracy decreases
Solution Approach 1:
The patent segments the feature extraction process into appearance features from individual windows and context features from relationships between windows. This segmentation allows the system to use computationally efficient appearance-based candidate selection while incorporating more sophisticated context-based relationships for improved detection accuracy.
Solution Approach 2:
The patent introduces context classification as an intermediary step between appearance-based candidate generation and final detection results. This intermediary stage refines the candidates by evaluating contextual relationships and structural information, improving detection accuracy without requiring direct computation of complex geometric relationships between all object parts.
4Reliability
If context classification is applied to all windows in the image, then detection accuracy is improved, but computational efficiency decreases
Solution Approach 1:
The patent performs preliminary appearance classification on all candidate windows to identify promising regions before applying computationally intensive context classification. This preliminary filtering ensures that context classification is applied only to a subset of relevant candidates, maintaining detection accuracy while preserving computational efficiency.
Solution Approach 2:
The patent applies context classification selectively to candidate windows that pass the appearance classification threshold, rather than to all windows in the image. This partial application of context classification maintains detection accuracy for relevant objects while avoiding the computational burden of processing the entire image space.
Data Source
AI summary
Objects in an image are classified by applying an appearance classifier to the image to determine candidates of the objects and statistics associated with the candidates, wherein the appearance classifier uses a set of windows, and the candidates are in selected windows. Then, a context classifier is applied only to the selected windows of the image to determine an identity, and location of objects in the image.