Image classification
Searched over 200M research papers for "image classification"
Summary
10 papers analyzed
These studies suggest that image classification can be effectively achieved using various methods such as Bayesian classifiers, edge direction-based features, k-NN classifiers, supervised and unsupervised techniques, deep learning networks, and SVMs, with high accuracy in different contexts.
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Introduction
Image classification is a critical task in computer vision, involving the categorization of images into predefined classes based on their visual content. This process is essential for various applications, including content-based image retrieval, digital archive management, and medical imaging. The challenge lies in effectively extracting and utilizing low-level visual features to achieve high-level semantic classification.
Key Insights
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Hierarchical Classification and Bayesian Methods:
- Hierarchical classification using binary Bayesian classifiers can effectively categorize images into multiple levels, such as indoor/outdoor, city/landscape, and specific landscape types (e.g., sunset, forest, mountain) with high accuracy .
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Feature Saliency and Edge Direction:
- Edge direction-based features have been identified as highly discriminative for classifying city images versus landscapes, achieving significant accuracy with weighted k-NN classifiers .
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Low-Level Features for High-Level Classification:
- Low-level features such as color histograms, color coherence vectors, DCT coefficients, and edge direction histograms are crucial for effective image classification. These features can be used to build indices for image databases and achieve high classification accuracy .
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Supervised and Unsupervised Techniques:
- Supervised techniques like k-NN and SVM, as well as unsupervised techniques like K-means and ISODATA, are commonly used for image classification. Deep learning networks are also employed for large images due to their efficiency in computing hidden features automatically.
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Image Set Classification:
- Image set classification, which involves comparing query sets with gallery sets, offers advantages over single image-based methods by handling severe changes in appearance and making more robust decisions.
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Digital Archive Management:
- Image classification techniques, such as segmenting images into non-overlapping blocks and extracting color and texture features, are effective for managing digital archives. SVM classifiers can be used to annotate and verify image categories efficiently.
Conclusion
Image classification leverages various techniques and features to categorize images into meaningful classes. Hierarchical classification and Bayesian methods, edge direction-based features, and both supervised and unsupervised techniques play significant roles in achieving high accuracy. Additionally, image set classification and digital archive management benefit from these methodologies, highlighting the versatility and importance of image classification in different domains.
Sources and full results
Most relevant research papers on this topic
4
IMAGE CLASSIFICATION: A SHORT REVIEW
4
0 Citations
2014
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