Get Complex sciences, 1 conf PDF

By Jie Zhou

ISBN-10: 3642024653

ISBN-13: 9783642024658

This e-book constitutes the completely refereed post-conference lawsuits of the 1st foreign convention on complicated Sciences, complicated 2009, held in Shanghai, China, in February 2009.

The 227 revised complete papers provided including 23 papers from 5 collated workshops (COART, ComplexCCS, ComplexEN, MANDYN, SPA) have been conscientiously reviewed and chosen. The papers tackle the next themes: concept of artwork and track, causality in complicated platforms, engineering networks, modeling and research of human dynamics, social physics and its functions, constitution and dynamics of complicated networks, advanced organic platforms, complicated fiscal systens, complicated social structures, complicated engineering platforms, in addition to advanced platforms methods.

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We use a semi-automatic object identification algorithm to identify objects. Either by experts or by using a training set, the properties of objects and relations between objects are determined. Object classification is done by: – Low-level feature values like color distribution, shape, texture. – Spatial knowledge. – Spatio-temporal change. (A set of consecutive key frames must be searched to gain knowledge for this property) Extraction phase starts when we have enough knowledge to separate one object from others.

Given a query q, a retrieval system then simply computes the scores {F (q, p), ∀p ∈ P } and ranks the pictures of P by decreasing scores. The effectiveness of such a system is hence mainly determined by the choice of an appropriate function F . In fact, optimal retrieval performance would be achieved if F satisfies / R(q), F (q, p+ ) > F (q, p− ), ∀q, ∀p+ ∈ R(q), ∀p− ∈ (1) where R(q) refers to the pictures of P which are relevant to q. In other words, if F satisfies (1), the retrieval system will always rank the relevant pictures above the non-relevant ones.

Such models include, for instance, CrossMedia Relevance Models (CMRM) [3], Latent Dirichlet Allocation (LDA) [5] or Probabilistic Latent Semantic Analysis (PLSA) [6]. In this paper, we introduce an alternative to these approaches. The proposed model, Passive-Aggressive Model for Image Retrieval (PAMIR), relies on discriminative learning. This means that the model parameters are not selected to maximize the likelihood of some annotated training data; they are instead selected to maximize the retrieval performance of the model over a set of training queries.

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Complex sciences, 1 conf by Jie Zhou

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