記憶に残る地図：視覚的位置認識における場所の再定義フレームワーク
Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition
人間の記憶しやすさに着想を得て、画像のエントロピーと静的コンテンツから「記憶度」を評価し、視覚的位置認識のための地図を構築するフレームワークを提案。混同しやすい画像を含むデータセットESSEX3IN1も作成し、既存手法の性能を大幅に向上させた。
著者: Mubariz Zaffar, Shoaib Ehsan, Michael Milford, Klaus Mcdonald Maier
分類: cs.CV, cs.RO
原文アブストラクト
This paper presents a cognition-inspired agnostic framework for building a map for Visual Place Recognition. This framework draws inspiration from human-memorability, utilizes the traditional image entropy concept and computes the static content in an image; thereby presenting a tri-folded criterion to assess the 'memorability' of an image for visual place recognition. A dataset namely 'ESSEX3IN1' is created, composed of highly confusing images from indoor, outdoor and natural scenes for analysis. When used in conjunction with state-of-the-art visual place recognition methods, the proposed framework provides significant performance boost to these techniques, as evidenced by results on ESSEX3IN1 and other public datasets.
