By Guoping Qiu, Kin Man Lam, Hitoshi Kiya, Xiang-Yang Xue, C.-C. Jay Kuo, Michael S. Lew
This ebook constitutes the court cases of the eleventh Pacific Rim convention on Advances in Multimedia info Processing, held in Shanghai in September 2010.
Read Online or Download Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings PDF
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Extra resources for Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings
The remainder of this article is organized as below: In Section 2, we present basic concept of query and query formulation. Firstly, two basic ways to formulate query for rich media data are introduced and then we explore main challenges for designing query formulation scheme under social network environment. Section 3 provides review of existing works related to database access method. We also present potential research directions and possible technical solutions. In Section 4, we mainly focus on CF based recommendation systems and give a comprehensive discussion about open problems and possible methods for solving those problem.
To discover the eﬀect of the depth of layers on dimensionality reduction, we constructed four diﬀerent auto-encoders with diﬀerent number of layers to both datasets, the number of layers of the auto-encoders were 2,3,4 and 6. All the autoencoders transformed the data into a 50 dimension space. 5 shows that on both datasets, when the layer of architecture gets deeper, the classiﬁcation performance is not improve correspondingly. The best performance is achieved in a depth where more dependency of data could be captured while less noise are involved, so that the architecture could not be too deep.
Its main advantages include simplicity and robustness. However, search engines based on QIF generally suffer from low query accuracy and poor scalability to cover complexity of possible queries. The expectation for query fusion is that query constructed with combining various modalities is more informative and completed than the ones based on single input. In fact, different modalities could provide different levels of contribution to final performance of retrieval process. This observation suggests that if search algorithm can be tuned based on user’s search intention, better performance can be expected and the corresponding fusion strategy is called Query Dependent Fusion (QDF).