SaowalukC. WATANAPA•õa), Member, Bundit THIPAKORN•õ•õb), and Nipon CHAROENKITKARN•õc), Nonmembers SUMMARY 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.
Nowadays, competition in the online business of tourism esp. hotels and resorts is noticeably fierce. Using a kind of cyber brokerage system, called affiliate, in conducting marketing becomes more and more popular marketing weapon for tourism industry. it is viewed as an effective marketing tool that can raise the exposure of products and chance to establish credibility and strength of brand image. The objective of this research is to study relationships between factors in the system of affiliate marketing and the rent intention. Online questionnaire-based data were collected from Thai consumers who used to make room reservations via affiliate marketing system. Experimental results show that the trust in publisher in the affiliate system is the key factor significantly affecting the rent intention.
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