Semantics-based Image Retrieval
| Autor: | Wu, Wei-Liang, 吳韋良 |
|---|---|
| Rok vydání: | 2016 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 Image search is an important technique in multimedia applications, in which image retrieval is a common technology for image search. Given a query image, the goal is to retrieve relevant images from an image database. Most previous research studies rely on important features extracted from images to calculate the similarity of two images. One of the drawbacks of this approach is that it focuses on the image-specific features without considering semantics of the images. Therefore, the images that are semantically related to query images but highly differ in image features will not be the candidates of the retrieval. Additionally, many photo websites allow users to provide descriptions or tags for the photos they uploaded, inspiring us to use the image itself and its description to propose a semantics-based image retrieval framework by using machine learning techniques. The key idea behind the proposed method is to extract important objects from the query image, and classified the extracted objects as predefined labels for this query image. Then, we project the labels and the descriptions in the data set to the same latent space by using deep neural networks, and calculate semantic similarity in the latent space. This thesis conducts experiments on Flickr data set and evaluates the results with the average irrelevant image number of the searching results. The experimental results indicate that when only using an image as query, the retrieved results are much acceptable than other methods' results. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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