Image Processing Apparatus Shape-Based Category Classification
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Solution Overview
Problem
Current image processing technologies face challenges in efficiently analyzing and managing large volumes of images captured by mobile devices, particularly in determining object categories and providing user-friendly operations by automatically tagging and classifying images.
Innovation Solution
An image processing apparatus and method that acquires images, extracts object shapes, determines categories, and stores images with additional information, including keywords, using feature map models and a cascade classifier to facilitate quick and accurate object detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If object detection technology is used to analyze image information, then understanding of image information is improved, but processing time and computational complexity increase
Solution Approach 1:
The image processing is divided into multiple stages: first extracting shape information, then determining categories based on shape, and finally performing detailed analysis only on identified objects. This segmentation allows rapid shape-based filtering before more computationally intensive analysis, reducing overall processing time while maintaining comprehensive image understanding.
Solution Approach 2:
Shape extraction and category determination are performed as preliminary steps before detailed object analysis. By pre-processing images to identify and categorize objects based on shape features first, the system prepares structured information that accelerates subsequent detection and analysis operations, reducing total processing time.
2Measurement precision
If manual tagging and classification of images is performed, then image management accuracy is improved, but user time and operational effort increase
Solution Approach 1:
The system automatically performs image tagging and classification by extracting shape information and determining categories without requiring manual user intervention. The automatic category determination system analyzes image shapes and assigns appropriate categories and keywords autonomously, eliminating the need for users to manually tag images while maintaining high classification accuracy.
Solution Approach 2:
Manual mechanical tagging operations are replaced with automated computational processes. The system uses shape extraction algorithms and category determination mechanisms to automatically generate tags and classifications, substituting human manual work with automated image processing techniques that achieve comparable or superior accuracy while saving user time.
3Measurement precision
If comprehensive object detection is performed on all images, then detection accuracy is improved, but processing speed decreases
Solution Approach 1:
The system applies different processing levels to different regions and objects in images. Shape extraction is performed on all images, but detailed category determination and object detection are focused specifically on identified target objects rather than analyzing every pixel uniformly. This localized approach maintains high detection accuracy for relevant objects while reducing overall processing requirements.
Solution Approach 2:
The system performs partial analysis by focusing on shape extraction and category determination as sufficient for many applications, rather than always performing complete comprehensive object detection. For routine image management tasks, shape-based category determination provides adequate accuracy at lower computational cost, reserving more intensive detection methods for cases requiring higher precision.
Data Source
AI summary
Processing an image includes acquiring, by the image processing apparatus, a target image, extracting a shape of a target object included in the target image, determining a category including the target object based on the extracted shape, and storing the target image by mapping the target image with additional information including at least one keyword related to the category.