We present a new histogram distance family, the Quadratic-Chi(QC). QC members are Quadratic-Form distances with a cross-bin χ
2-like normalization. The cross-bin χ
2-like normalization reduces the effect of large bins having undue influence. Normalization was shown to be helpful in many cases, where the χ
2 histogram distance outperformed the L
2 norm. However, χ
2 is sensitive to quantization effects, such as caused by light changes, shape deformations etc. The Quadratic-Form part of QC members takes care of cross-bin relationships (e.g. red and orange), alleviating the quantization problem. We present two
new cross-bin histogram distance properties: Similarity-Matrix-Quantization-Invariance and Sparseness-Invariance and show that
QC distances have these properties. We also show that experimentally they boost performance. QC distances computation time
complexity is linear in the number of non-zero entries in the bin-similarity matrix and histograms and it can easily be
parallelized. We present results for image
retrieval using the Scale Invariant Feature Transform (SIFT) and color image descriptors. In addition, we present results for shape classification using Shape Context
(SC) and Inner Distance Shape Context (IDSC). We show that the new
QC members outperform state of the art distances for these tasks, while having a short running
time. The experimental results show that both the cross-bin property and the normalization are important.
