Conditional Random Fields : Probabilistic Models for Segmenting and Labeling Sequence Data

主要介紹CRF的概念，參考一下兩篇相關文獻 :
[1] J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In International Conference on Machine Learning, 2001.
[2] Hanna M. Wallach. Conditional Random Fields: An Introduction. University of Pennsylvania CIS Technical Report MS-CIS-04-21.

過去針對sequential data進行segmenting或是labeling的動作，有HMM、MEMMs等技術，以Generative和Discriminative Model區分之。CRFs則是近年來提出的新穎做法，其模型為無向圖，在給定一個觀察序列下，求算整體狀態序列的條件機率。參數的估計方式原先提出iterative scaling algorithm找出Log-likelihood objective function中的最大值，而後的相關文獻中也針對參數預估提出不同的計算方式，以增快求算的效率。

Conditional Random Fields

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Semantics In Digital Photos A Contenxtual Analysis

Interpreting the semantics of an image is a hard problem.
However, for storing and indexing large multimedia lections,it is essential to build systems that can automatically extract semantics from images. In this research we show how we can fuse content and context to extract semantics from digital photographs. Our experiments show that if we can properly model context associated with media, we can interpret semantics using only a part of high dimensional content data.

Semantics In Digital Photos A Contenxtual Analysis

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