What are high-level features in CBIR?
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High-level features in Content-based Image Retrieval (CBIR) refer to features that capture the semantic content of an image and help reduce the "semantic gap" between low-level features and high-level concepts. These features play a crucial role in the success of CBIR systems. One approach to extracting high-level features is to use pre-trained network models from deep-learning convolution networks that were trained for large image classification problems. This approach has been shown to produce superior results compared to many contemporary CBIR systems . Deep learning methods, including deep features, have gained attention in CBIR research and have been compared with state-of-the-art low-level features such as SIFT, SURF, HOG, LBP, and LTP. The experimental results demonstrate the discriminative power of deep features and their ability to improve the representation of image content .
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01 Jan 2020 | The paper does not explicitly mention what high-level features are in CBIR. |
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