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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208
10
章 コンピュータビジョン：パターン認識
両方とも自然物を背景としています。また、対象物の中に滑らかな領域が多く含まれます。そのた
め、テクスチャを特徴量として用いても、良い結果にはならないでしょう。
前と同じ特徴量を用いた場合、ロジスティック回帰を用いると正解率は55％になります（交差検定を
用いて評価）。4つのクラスの分類問題としては悪すぎるということはありませんが、めざましい結果
であるとは言えません。それでは他の手法を使って、改善していきたいと思います。実際、最高の結
果を出すためには、テクスチャ特徴量を他の手法と組み合わせる必要があるかもしれません。しか
し、ここでは、まずはじめに局所特徴量（local feature）について紹介したいと思います。
10.4
局所特徴量による記述
コンピュータビジョンの分野で比較的最近になって提案された手法に局所特徴量に基づいた手法が
あります。局所特徴量（Local features）とは画像の局所的な領域を対象に計算される特徴量です
。先
ほどの例では画像全体を対象として特徴量を計算しましたが、それとは対照的です。mahotasはその
ような特徴量をいくつかサポートしています。SURF（Speeded Up Robust Features）や、最も有名で
あり、この分野のパイオニアであるSIFT（Scale-Invariant Feature Transform）などがあります。これ
らの局所特徴量は回転や照明変化にロバストであるように設計されて
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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.