Showing papers by "Vincent Oria published in 2012"

Dimensional Testing for Multi-step Similarity Search

Abstract: In data mining applications such as subspace clustering or feature selection, changes to the underlying feature set can require the reconstruction of search indices to support fundamental data mining tasks. For such situations, multi-step search approaches have been proposed that can accommodate changes in the underlying similarity measure without the need to rebuild the index. In this paper, we present a heuristic multi-step search algorithm that utilizes a measure of intrinsic dimension, the generalized expansion dimension (GED), as the basis of its search termination condition. Compared to the current state-of-the-art method, experimental results show that our heuristic approach is able to obtain significant improvements in both the number of candidates and the running time, while losing very little in the accuracy of the query results.

Guest editorial: content, concept and context mining in social media

Abstract: In the last few years, we have witnessed an exponentially growing amount of social media data from social networks, mainly driven by the huge popularity of Web and the rapid advances of multimedia and Internet technologies. Different from conventional data types, social media data are multi-model in nature, including content such as images, audio, and videos, concept such as discussion topic, tag, and annotation, and context such as links, profile, timestamp, and click-through. All these content, concept and context are essential data sources for mining semantics, user intents, trends, knowledge, etc., from social media data, as well as enabling intelligent applications on social media services. This rich media type has raised many new research challenges, ranging from large-scale social media content analysis and mining, concept discovery and monitoring, context-based services, to many other exciting new opportunities. The aim of this special issue is to bring out state-of-the-art research in this area and discover directions for future research. The special issue will serve as a forum for recent advances in the field, with an emphasis on mining the “3 C” (Content, Concept and Context). It received an enthusiastic response. Among 19 submissions, 5 papers were selected after several rounds of rigorous review by the guest editors and the invited reviewers. World Wide Web (2012) 15:115–116 DOI 10.1007/s11280-011-0142-4

Recognition and Summarization of Chord Progressions and Their Application to Music Information Retrieval

Abstract: Accurate and compact representation of music signals is a key component of large-scale content-based music applications such as music content management and near duplicate audio detection. This problem is not well solved yet despite many research efforts in this field. In this paper, we suggest mid-level summarization of music signals based on chord progressions. More specially, in our proposed algorithm, chord progressions are recognized from music signals based on a supervised learning model, and recognition accuracy is improved by locally probing n-best candidates. By investigating the properties of chord progressions, we further calculate a histogram from the probed chord progressions as a summary of the music signal. We show that the chord progression-based summarization is a powerful feature descriptor for representing harmonic progressions and tonal structures of music signals. The proposed algorithm is evaluated with content-based music retrieval as a typical application. The experimental results on a dataset with more than 70,000 songs confirm that our algorithm can effectively improve summarization accuracy of musical audio contents and retrieval performance, and enhance music retrieval applications on large-scale audio databases.