Image Recognition Model Using Texture Segmentation for Multi-Object Classification
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Solution Overview
Problem
Existing image recognition technologies face challenges in accurately classifying multiple objects of different categories within a single image, such as plants with flowers and leaves of varying sizes, leading to lower accuracy and efficiency.
Innovation Solution
An electronic device equipped with an image recognition system that employs a method involving segmentation, texture feature extraction using Gabor filters, grayscale level co-occurrence matrix determination, and statistical calculations to generate an object recognition model for effective classification of image objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image recognition methods are used to classify multiple objects in a single image, then the system is simple to implement, but the classification accuracy decreases when multiple objects of different categories are present
Solution Approach 1:
The patent divides the image processing task into multiple stages: first segmenting the image into multiple candidate regions, then extracting texture features from each region, and finally classifying each region independently. This segmentation approach enables accurate classification of multiple objects in a single image by treating each object region separately rather than attempting to classify the entire image at once.
Solution Approach 2:
The patent extracts texture features from segmented image regions using Gabor filters and grayscale level co-occurrence matrices. By extracting specific texture characteristics from each candidate region, the system can distinguish between different object categories (such as flowers versus leaves) based on their unique texture patterns, thereby improving classification accuracy.
2Productivity
If traditional image recognition methods are used, then the implementation is straightforward, but the recognition efficiency decreases when handling multiple objects
Solution Approach 1:
By segmenting the image into multiple candidate regions first, the system can process each region independently and in parallel. This segmentation strategy improves recognition efficiency by avoiding the need to reprocess the entire image for each object classification, thereby reducing redundant computations while handling multiple objects.
Solution Approach 2:
The patent performs preliminary segmentation and texture feature extraction before the actual classification step. By preparing candidate regions and their texture features in advance, the system reduces the computational burden during the final classification phase, thereby improving overall recognition efficiency when multiple objects are present.
Data Source
AI summary
A method for recognizing different object-categories within images based on texture classification of the different categories, which is implemented in an electronic device, includes extracting texture features from block images segmented from original images according to at least one Gabor filter; determining a grayscale level co-occurrence matrix of each block image according to the texture features; calculating texture feature statistics of each block image according to the grayscale level co-occurrence matrix; training and generating an object recognition model using the texture features and the texture feature statistics; and recognizing and classifying at least one object in original image according to the object recognition model.