Due to its low storage cost and high query accuracy, Product Quantization (PQ) has been widely used for approximate nearest neighbor (ANN) search. However, almost all existing PQ-based methods use the nearest clustering center as the codeword, which might not fully utilize the information of distances from data points to clustering centers. In this paper, we propose a novel codeword expansion method for PQ-based methods, called Quarter-point Codeword Expansion (QCE), by estimating the distances from the query points to the database points using the quarter points instead of the clustering centers. The distances can be computed more precisely and it will result in a lower distortion using QCE, which is also a general method could be used to improve all PQ-based methods. Extensive experiments on approximate nearest neighbor search show that PQ-based methods with QCE can outperform the state-of-the-art.

Product display in e-commerce commonly uses static images or videos. In this paper, we design a novel video composition system “RotateView” which displays real-world products videos on the mobile phone in an interactive and 3D-like way. The system estimates the rotation direction of products using optical flow, segments products using an improved version of online adaptive video object segmentation, and adjusts motion and color for composition. The rotation direction estimation methods are evaluated on a video dataset of 1500 rotated videos that are uploaded by sellers, and on our collected real-world video object segmentation (RVOS) dataset. On our RVOS dataset, experimental results show an improvement in segmentation over the state of the art. Moreover, the proposed algorithms for motion and color adjustment are evaluated using extensive user studies. The RVOS dataset can be downloaded viahttps://realvos.github.io/for further studies and code will be made available.