Titlebook: Artificial Neural Networks - ICANN 2006; 16th International C Stefanos Kollias,Andreas Stafylopatis,Erkki Oja Conference proceedings 2006 S

现存

Content-Based Coin Retrieval Using Invariant Features and Self-organizing Maps1 or L2 similarity measures lead to excellent retrieval capabilities. Finally, color quantization of the database images using self-organizing maps not only leads to memory savings but also it is shown to even improve retrieval accuracy.

Lucubrate

IRATE

mendacity

Feuerfeste Baustoffe in Siemens-Martin-Öfenual features to evaluate the potential of our approach in bridging the gap from visual features to semantic concepts by the use textual presentations. Our initial results show a promising increase in retrieval performance.

古代

https://doi.org/10.1007/978-3-7091-7948-2ructure of this metric and proposes a method to update it very efficiently based on the GM models of the relevant and irrelevant images characterized by the user. We show with experiments the merits of the proposed methodology.

MERIT

https://doi.org/10.1007/978-3-662-28736-1erefore, we have implemented a set of comparison methods, the neural network and an extension to the learning rule to include a human as a teacher. First results are promising and show that the approach is valuable for learning human judged time-series similarity with a neural network.

垄断

愤世嫉俗者

起草

A Relevance Feedback Approach for Content Based Image Retrieval Using Gaussian Mixture Modelsructure of this metric and proposes a method to update it very efficiently based on the GM models of the relevant and irrelevant images characterized by the user. We show with experiments the merits of the proposed methodology.

Alveoli

Learning Time-Series Similarity with a Neural Network by Combining Similarity Measureserefore, we have implemented a set of comparison methods, the neural network and an extension to the learning rule to include a human as a teacher. First results are promising and show that the approach is valuable for learning human judged time-series similarity with a neural network.