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首页> 外文会议>Proceedings of the 2006 International Conference on Machine Learning and Cybernetics >RELEVANCE FEEDBACK TECHNIQUE FOR CONTENT-BASED IMAGE RETRIEVAL USING NEURAL NETWORK LEARNING
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RELEVANCE FEEDBACK TECHNIQUE FOR CONTENT-BASED IMAGE RETRIEVAL USING NEURAL NETWORK LEARNING

机译：基于神经网络学习的基于内容的图像相关性反馈技术

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摘要

Relevance feedback (RF) is an interactive process in content-based image retrieval (CBIR), which refines the retrievals to a particular query by using user's feedback on previously retrieved results. In this paper, by changing the process of relevance feedback into a learning problem of neural network, a relevance feedback technique for content-based images retrieval by neural network learning (NELIR) is introduced, which can improve user interaction with image retrieval systems by fully exploiting similarity information. NELIR can describe the distribution of positive feedback sample images in feature space with a set of neighboring clusters produced through constructing neural network, for accurately reflecting their semantic relevance. In particular, constructing neural network is dynamic. The neural network depends on which images are retrieved in response to the query. On the other hand, NELIR is independent of the specific feature extraction and similarity measure. Thus, it may be embedded in many current CBIR systems to improve the performance of image retrieval. The performance of a prototype system using NELIR is evaluated on a database of 2,000 images. Experimental results demonstrate improved performance compared with a traditional CBIR system without NELIR algorithm using the same image similarity measure.
机译：相关性反馈（RF）是基于内容的图像检索（CBIR）中的交互式过程，该过程通过使用用户对先前检索到的结果的反馈，将检索结果细化为特定查询。本文通过将相关反馈过程转变为神经网络的学习问题，提出了一种基于神经网络学习（NELIR）的基于内容的图像检索相关反馈技术，可以充分改善用户与图像检索系统的交互利用相似性信息。 NELIR可以通过构造神经网络来描述特征反馈与一组相邻簇在特征空间中的正反馈样本图像的分布，以准确反映它们的语义相关性。特别地，构造神经网络是动态的。神经网络取决于响应于查询检索到哪些图像。另一方面，NELIR独立于特定的特征提取和相似性度量。因此，可以将其嵌入许多当前的CBIR系统中以提高图像检索的性能。使用NELIR的原型系统的性能在2,000张图像的数据库中进行了评估。实验结果表明，与不使用NELIR算法的传统CBIR系统相比，使用相同的图像相似性度量，性能得到了改善。

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