Which properties are needed for image features in the context of content-based image retrieval?
Best insight from top research papers
Image features in the context of content-based image retrieval need to have properties such as colour, texture, and shape . These features are extracted from the images and used to represent the visual contents of the images in a database. The challenge in content-based image retrieval is reducing the semantic gap between machine and human conceptual understanding . To address this challenge, various visual features have been proposed, including Gary Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Discrete Wavelet Transform (DWT) texture features . Additionally, the fusion of different features, such as spatial color information and shaped extracted features, can improve the effectiveness of image representation . The use of gradient and texture features has also been found to be effective in retrieving images of similar objects from categorized datasets . Overall, the properties of image features in content-based image retrieval should enable effective representation and similarity measures between query and database images.
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| Papers (5) | Insight |
|---|---|
29 Jan 2021 1 Citations | The paper mentions that the properties needed for image features in the context of content-based image retrieval are color, texture, and shape. |
30 Jun 2019 1 Citations | The paper mentions that the present CBIR systems have been limited by their use of only primitive features, indicating that more advanced and semantic-level features are needed for content-based image retrieval. |
11 Citations | The paper mentions that colour and texture features are needed for image representation in content-based image retrieval. |
93 Citations | The paper mentions that the image features needed for content-based image retrieval include shape, texture, color, and spatial information. |
| The paper mentions that gradient and texture features are used for content-based image retrieval. |
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