Dictionary Selection for Image Detection Adaptation
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Solution Overview
Problem
Existing methods for detecting human figures in images face performance degradation due to differences in photography scenes and personal appearances between initial learning and detection, such as varying lighting, camera angles, and background textures, limiting the improvement in identification accuracy.
Innovation Solution
An image processing apparatus that includes multiple dictionaries storing feature information and imaging directions for different scenes, a detecting unit, an estimating unit to determine the imaging direction, a selecting unit to choose the most suitable dictionary, and a learning unit to perform additional learning based on the detected results, allowing for precise adaptation of the detector dictionary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single dictionary is learned in advance through machine learning, then the detection process is simple and fast, but detection performance degrades when photography scenes or personal appearances differ from learning conditions
Solution Approach 1:
The patent segments the single dictionary into multiple scene-specific dictionaries (indoor scene dictionary, outdoor scene dictionary, etc.). Each dictionary is specialized for a particular photography scene, allowing the system to select the appropriate dictionary based on the current scene type. This segmentation resolves the contradiction by maintaining high detection performance within each scene while providing adaptability across different scenes through dictionary selection.
Solution Approach 2:
The patent changes the parameter of scene type (indoor, outdoor, etc.) to create different dictionaries. By organizing dictionaries according to scene parameters and selecting the appropriate dictionary based on the current scene, the system maintains optimal detection performance for each scene type while adapting to various photography environments.
2Reliability
If multiple scene-specific dictionaries are prepared, then detection performance is maintained across different scenes, but the system complexity increases
Solution Approach 1:
The patent segments dictionaries by scene type, creating a manageable organization structure. Instead of one complex universal dictionary, multiple specialized dictionaries are created, each optimized for specific scenes. The scene determination unit adds minimal complexity by simply categorizing the current scene and selecting the corresponding dictionary, making the overall system complexity acceptable while achieving high detection performance.
Solution Approach 2:
The scene determination unit acts as an intermediary between the image input and the dictionary selection process. It analyzes the input image to determine the scene type and selects the appropriate dictionary, thereby managing the complexity of having multiple dictionaries without requiring the detection algorithm itself to be overly complex.
3Measurement precision
If additional learning is performed using samples from similar photography scenes, then detection accuracy improves, but learning time increases when scene differences are significant
Solution Approach 1:
The patent performs preliminary organization of learning samples by scene type before additional learning. By pre-categorizing samples into different scene types and preparing scene-specific dictionaries in advance, the system can quickly select the appropriate dictionary during additional learning without needing to process all samples from all scenes, thereby reducing learning time while maintaining high identification accuracy.
Solution Approach 2:
The patent changes the learning approach by parameterizing it according to scene type. Instead of performing uniform additional learning on all samples, the system performs scene-specific additional learning by selecting samples and dictionaries matched to the current scene type, which accelerates convergence and reduces learning time while maintaining high accuracy.
Data Source
AI summary
An image processing apparatus includes a plurality of dictionaries configured to store a feature of an object and information on an imaging direction in a scene for each kind of imaged scene, a detecting unit configured to detect an object with reference to at least one of the plurality of dictionaries in the scene in which the object has been imaged and which is to be learned, an estimating unit configured to estimate the imaging direction the detected object, a selecting unit configured to select one dictionary from the plurality of dictionaries based on the imaging direction estimated by the estimating unit and the information on the imaging direction in each of the plurality of dictionaries, and a learning unit configured to learn the dictionary selected by the selecting unit, based on a detection result produced by the detecting unit.