Music emotion annotation by machine learning
- Authors: Cheung, Wai , Lu, Guojun
- Date: 2008
- Type: Text , Conference paper
- Relation: Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing p. 580-585
- Full Text: false
- Reviewed:
- Description: Music emotion annotation is a task of attaching emotional terms to musical works. As volume of online musical contents expands rapidly in recent years, demands for retrieval by emotion are emerging. Currently, literature on music retrieval using emotional terms is rare. Emotion annotated data are scarce in existing music databases because annotation is still a manual task. Automating music emotion annotation is an essential prerequisite to research in music retrieval by emotion, for without which even sophisticated retrieval methods may not be very useful in a data deficient environment. This paper describes a machine learning approach to annotate music using a large number of emotional terms. We also estimate the training data size requirements for a workable annotation system. Our empirical result shows that 1) the task of music emotion annotation could be modelled using machine learning techniques to support a large number of emotional terms, 2) the combination of sampling method and data-driven detection threshold is highly effective in optimizing the use of existing annotated data in training machine learning models, 3) synonymous relationships enhance the annotation performance and 4) the training data size requirement is within reach for a workable annotation system. Essentially, automatic music emotion annotation enables music retrieval by emotion to be performed as a text retrieval task.
Region-based image retrieval with high-level semantics using decision tree learning
- Authors: Liu, Ying , Zhang, Dengsheng , Lu, Guojun
- Date: 2008
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 41, no. 8 (2008), p. 2554-2570
- Full Text: false
- Reviewed:
- Description: Semantic-based image retrieval has attracted great interest in recent years. This paper proposes a region-based image retrieval system with high-level semantic learning. The key features of the system are: (1) it supports both query by keyword and query by region of interest. The system segments an image into different regions and extracts low-level features of each region. From these features, high-level concepts are obtained using a proposed decision tree-based learning algorithm named DT-ST. During retrieval, a set of images whose semantic concept matches the query is returned. Experiments on a standard real-world image database confirm that the proposed system significantly improves the retrieval performance, compared with a conventional content-based image retrieval system. (2) The proposed decision tree induction method DT-ST for image semantic learning is different from other decision tree induction algorithms in that it makes use of the semantic templates to discretize continuous-valued region features and avoids the difficult image feature discretization problem. Furthermore, it introduces a hybrid tree simplification method to handle the noise and tree fragmentation problems, thereby improving the classification performance of the tree. Experimental results indicate that DT-ST outperforms two well-established decision tree induction algorithms ID3 and C4.5 in image semantic learning.