Light-weight binary code embedding of local feature distribution in image search

Binary code embedding, which aims to generate compact and discriminative binary codes from local image features, can remarkably improve the image search performance by compensating the quantization error in Bag-of-Words (BoW) model. However, the relationship between local features and their neighbors are often ignored by existing embedding schemes, while such information of spatial distribution can greatly improve the discriminative ability of binary codes. Toward this end, this paper proposes two light-weight schemes for binary code embedding that take the spatial distribution of local features into account. These two schemes, including the Content Similarity Embedding (CSE) and Scale Similarity Embedding (SSE), are highly flexible in balancing the computational cost as well as the image search performance. Experimental results on several public benchmarks show that, with the proposed two embedding schemes, image search achieves comparable performance with state-of-the-arts with much lower computational cost and memory usage.