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Vector-quantized local features frequently used in bag-of-visual-wordsapproaches are the backbone of popular visual recognition systems due to boththeir simplicity and their performance. Despite their success,bag-of-words-histograms basically contain low-level image statistics (e.g.,number of edges of different orientations). The question remains how muchvisual information is "lost in quantization" when mapping visual features tocode words? To answer this question, we present an in-depth analysis of theeffect of local feature quantization on human recognition performance. Ouranalysis is based on recovering the visual information by inverting quantizedlocal features and presenting these visualizations with different codebooksizes to human observers. Although feature inversion techniques are around forquite a while, to the best of our knowledge, our technique is the firstvisualizing especially the effect of feature quantization. Thereby, we are nowable to compare single steps in common image classification pipelines to humancounterparts.

Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods