研究目的
Investigating methods for accelerating image retrieval and classification in large datasets using hand-crafted features.
研究成果
The book presents effective methods for fast image classification and retrieval using hand-crafted features, with implementations in relational databases. Techniques like boosting fuzzy classifiers and bag-of-features show promise in accuracy and speed. Future work could integrate learned features and improve robustness to noise and occlusions.
研究不足
The methods rely on hand-crafted features, which may not be as robust as learned features in deep learning. Performance depends on image quality and complexity. Database implementations are specific to Microsoft SQL Server and may require adaptation for other systems. Computational cost can be high for large datasets.
1：Experimental Design and Method Selection:
The book employs various computer vision methods such as SIFT, SURF, Canny edge detection, k-means clustering, and fuzzy logic for feature detection, indexing, and classification. Theoretical models include AdaBoost for boosting fuzzy classifiers and bag-of-features for image representation.
2：Sample Selection and Data Sources:
Datasets used include PASCAL Visual Object Classes (VOC) dataset and Corel Database for Content-based Image Retrieval. Images are divided into training and testing sets (e.g., 90% training, 10% testing).
3：List of Experimental Equipment and Materials:
Software tools include OpenCV library, Emgu CV, Microsoft SQL Server with CLR integration, and custom implementations in C# and C++. Hardware includes virtual machines with specifications like 8 GB RAM, Intel Xeon X5650 2.67 GHz processor.
4：67 GHz processor.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Steps involve feature extraction (e.g., SIFT, SURF), clustering (e.g., k-means), descriptor generation, indexing, and comparison using distance measures. For database implementations, images are stored in FileTables, and UDTs are used for feature storage.
5：Data Analysis Methods:
Performance is evaluated using precision, recall, classification accuracy, and computational time. Statistical techniques include error calculation in boosting and SVM classification.
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