基於巨觀與微觀類別模型的一個機率式影像分類方法

A Probabilistic Approach for Image Categorized Based on Macro and Micro Classification Model

余松樺(Song-Hua Yu)

指導教授 ： 郭忠民

義守大學/電機資訊學院/資訊工程學系/碩士(2015年)

https://doi.org/10.6343/ISU.2015.00151

摘要

影像分類在現在是一個熱門的議題，而把數千數萬張的影像分類不是一件容易的工作；分類系統是對影像的每個類別做特徵影像分析，以此為基礎結合影像內容中的色彩特徵，並且應用於分析影像內容以協助影像分類的工作。
在本研究中，從資料庫中選出訓練樣本，以視覺字塊的方式提取影像特徵，並且將區塊依照特性分為巨觀字與微觀字兩種類型，將取出的視覺字訓練成巨觀字典與微觀視覺字典，由巨觀字典與微觀字典為基礎建立類別模型，每個模型獨特，不易混淆；而後以建立的類別模型為分類依據，已達成分類影像的目的。

關鍵字

視覺字典 ； 分類模型

並列摘要

This issue of image classification has received much attention recently; however, to classify huge amount of images into different categories is hard. For each category, classification system should first analyze features of image, which is described by visual words, and then the classification model is constructed. Finally, a probabilistic classifier is proposed to effectively category images.

The proposed algorithm is first collected training samples from the database, and extract image feature by visual patch. In addition, the patch divided into macro words and micro words according to patch content and then macro and micro visual dictionary is constructed. We then build classification models with representative and effective. Finally, the MAP based classifier is developed to classification image correctly. Simulation results show that the categorization scheme achieve surprising performance.

並列關鍵字

Bag of Visual Words

參考文獻

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