Search by Image: Deeply Exploring Beneficial Features for Beauty Product Retrieval

Searching by image is popular yet still challenging in e-commerce due to the extensive interference arising from (i) data variations (e.g., background, pose, visual angle, brightness) of real-world captured images and (ii) similar images in the query dataset. This article studies a practically meaningful problem of beauty product retrieval (BPR) by neural networks. We broadly extract different types of image features and raise an intriguing question that whether these features are beneficial to (i) suppress data variations of real-world captured images and (ii) distinguish one image from others which look very similar but are intrinsically different beauty products in the dataset, therefore leading to an enhanced capability of BPR. To answer it, we present a novel v ariable-attention neural network to understand the combination of m ultiple features (termed VM-Net) of beauty product images. Considering that there are few publicly released training datasets for BPR, we establish a new dataset with more than one million images classified into more than 20K categories to improve both the generalization and anti-interference abilities of VM-Net and other methods. We verify the performance of VM-Net and its competitors on the benchmark dataset Perfect-500K, where VM-Net shows clear improvements over the competitors in terms of \(MAP@7\) . The source code and dataset will be released upon publication.