Affective computing
Attributes of images in describing tasks
Information Processing and Management: an International Journal
Digital Image Processing
Experiments in speech recognition using a modular MLP architecture for acoustic modelling
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Spoken language analysis, modeling and recognition-statistical and adaptive connectionist approaches
Affective content detection using HMMs
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
eMoto: emotionally engaging interaction
Personal and Ubiquitous Computing
On detecting nonlinear patterns in discriminant problems
Information Sciences: an International Journal
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Effective classification and analysis of semantic contents are very important for the content-based indexing and retrieval of video database. Our research attempts to classify movie clips into three groups of commonly elicited emotions, namely excitement, joy and sadness, based on a set of abstract-level semantic features extracted from the film sequence. In particular, these features consist of six visual and audio measures grounded on the artistic film theories. A unique sieving-structured neural network is proposed to be the classifying model due to its robustness. The performance of the proposed model is tested with 101 movie clips excerpted from 24 award-winning and well-known Hollywood feature films. The experimental result of 97.8% correct classification rate, measured against the collected human-judges, indicates the great potential of using abstract-level semantic features as an engineered tool for the application of video-content retrieval/indexing.