Image Classification via Object Detection and Feature Vector Retrieval
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Solution Overview
Problem
Existing image classification models require retraining whenever new categories are added, struggle to recognize multiple objects in an image, and have poor recognition of small objects.
Innovation Solution
A method and system that utilize object detection, image recognition, and feature vector retrieval to classify images, allowing for easy expansion of classification categories without the need for retraining the image recognition model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional image classification models are used, then the model can classify images into predefined categories, but the model needs to be retrained every time a new category is added
Solution Approach 1:
The patent segments the image classification task into two independent parts: (1) object detection to identify objects and their positions, and (2) feature extraction to obtain visual features. By separating these functions, the system can add new categories without retraining the entire model, as only the feature extraction needs to be updated for new object types.
Solution Approach 2:
The patent introduces an intermediary feature vector representation that bridges object detection and classification. Instead of directly mapping images to categories, the system extracts intermediate feature vectors from detected objects and uses these vectors for similarity matching, enabling flexible category expansion without model retraining.
2Adaptability or versatility
If traditional image classification models are used, then the model outputs a single category, but the model cannot recognize multiple objects in an image
Solution Approach 1:
The patent applies segmentation by detecting multiple objects independently in the image and extracting features for each object separately. The system then aggregates these individual object features to produce a comprehensive classification result, enabling multi-object recognition without requiring a completely different model architecture.
Solution Approach 2:
The patent merges the results from multiple object detections and feature extractions to produce a unified classification output. By combining the feature vectors from all detected objects and using similarity matching against the database, the system achieves multi-object recognition while maintaining a relatively simple overall architecture.
3Measurement precision
If traditional image classification models are used, then the model can classify images, but the recognition of small objects is poor
Solution Approach 1:
The patent extracts and focuses on the visual features of detected objects separately from the overall image processing. By isolating the feature extraction step and applying it specifically to detected objects (regardless of size), the system can accurately recognize small objects without increasing the overall complexity of the feature extraction mechanism.
Solution Approach 2:
The patent performs object detection and feature extraction as preliminary actions before final classification. By pre-processing the image to identify and extract features from detected objects (including small ones) before the classification step, the system improves small object recognition accuracy without adding complexity to the main classification algorithm.
Data Source
AI summary
Method and system for classifying images, storage medium and terminal. The method includes: constructing an object vector retrieval library, storing object feature vectors and object names of stored objects; performing object detection on a to-be-classified image and obtaining an object image of a detected object contained in the to-be-classified image; performing image recognition on the object image of the detected object to obtain an object feature vector of the object image; and searching the object vector retrieval library for an object name of a first stored object of the stored objects whose object feature vector matches the object feature vector of the object image of the detected object, and using the object name of the first stored object as a category of the to-be-classified image. The present disclosure can accurately retrieve images and conveniently expand classification categories through object detection, image recognition, and feature vector retrieval.