October 2014
Intermediate to advanced
288 pages
6h 27m
Japanese
Content preview from 実践 機械学習システム
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204
10
章 コンピュータビジョン：パターン認識
mh.features.haralick
は4×13の配列を返します。一つ目の次元には4 つの方向（上・下・左・
右）について特徴量が計算されています。もし方向について興味がなければ、全方向の平均を用いる
こともできます。この関数を使えば、簡単に識別を行うことができます。
mahotasには他にも特徴量が実装されています。
Linear Binary Patterns
はテクスチャベー
スの特徴量であり、照明の変化に対してロバストな特性を持ちます。他にも違うタイプの特徴量があ
り（局所特徴量も含みます）、後ほど紹介していきます。
特徴量はクラス分類のためだけではない
特徴量を計算することで、何百万からなるピクセルの数を削減することができます。この結
果は、クラスタリング、回帰、次元削減などにも用いることができます。数百程度の数からな
る特徴量に対して次元削減のアルゴリズムを実行することで、何百万のピクセルからなる配
列をわずかな次元数に減らすことがで
きます。さらに
2
次元（もしくは
3
次元）にまで削減す
ると、ツールを用いて可視化することができます。
これらの特徴量を用いて、サポートベクターマシンなどの標準的なクラス分類手法を使用します
†
。
images = glob('simple-dataset/*.jpg')
features = []
labels = []
for im in images:
features.append(mh.features.haralick(im).mean(0)) ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.