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Multi-view product image queries can improve retrieval performance oversingle view queries significantly. In this paper, we investigated theperformance of deep convolutional neural networks (ConvNets) on multi-viewproduct image search. First, we trained a VGG-like network to learn deepConvNets representations of product images. Then, we computed the deep ConvNetsrepresentations of database and query images and performed single view queries,and multi-view queries using several early and late fusion approaches.  We performed extensive experiments on the publicly available Multi-ViewObject Image Dataset (MVOD 5K) with both clean background queries from theInternet and cluttered background queries from a mobile phone. We compared theperformance of ConvNets to the classical bag-of-visual-words (BoWs). Weconcluded that (1) multi-view queries with deep ConvNets representationsperform significantly better than single view queries, (2) ConvNets performmuch better than BoWs and have room for further improvement, (3) pre-trainingof ConvNets on a different image dataset with background clutter is needed toobtain good performance on cluttered product image queries obtained with amobile phone.

Multi-View Product Image Search Using Deep ConvNets Representations