Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.

Efficient content-based image retrieval based on multi-feature fusion

The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.

Appendiceal sonography in children: A retrospective analysis as a platform for potential implementation of diagnostic categories

- Background Sonography is an important diagnostic tool in children with suspected appendicitis. Reported accuracy and appendiceal visualisation rates vary significantly, as does the management of equivocal ultrasound findings. The aim of this study was to audit appendiceal sonography at a tertiary children's hospital, and provide baseline data for a future prospective study. - Summary of work Records of children who underwent ultrasound studies for possible appendicitis between January 2008 and December 2010 were reviewed. Variables included patient demographics, sonographic appendix characteristics, and secondary signs. Descriptive statistics and analysis using ANOVA, Mann-Whitney U test, and ROC curves were performed. Mater Human Research Ethic Committee approval was granted. - Summary of results There were 457 eligible children. Using a dichotomous diagnostic model (including equivocal results), sensitivity was 89.6%, specificity 91.6%, and diagnostic yield of 40.7%. ROC curve analysis of a 6mm diameter cut-off was 0.88 AUC (95% CI 0.80 to 0.95). - Discussion and conclusions Sonography is an accurate test for acute appendicitis in children, with a high sensitivity and negative predictive value. A diameter of 6mm as an absolute cut-off in a binary model can lead to false findings. Results were compared with available literature. Recent publications propose categorising diameter1 and integrating secondary signs2 to improve accuracy and provide more meaningful results to clinicians. This study will be a benchmark for future studies with multiple diagnostic categorisation.