Abstract
During the past few years, the Bag-of-Words (BoW) model based on SIFT features has been one of the most adopted approaches by the Content-Based Image Retrieval (CBIR) systems. However, these CBIR systems have shown some weaknesses and shortcomings especially for large scale image collections. This is due to two main causes: First, information is lost in the quantization step and second, the SIFT features describe only the local gradient. To tackle these issues, we proposed to take advantage of the Hamming Embedding, soft assignment and multiple assignment techniques, on the one hand, and to fuse SIFT and color features at the indexing level in a multi-index structure, on the other. In fact, in this paper, generic and non-parametric image retrieval schemes as well as a novel multi-IDF design based on multi-index structure were proposed.
Extensive experiments were conducted on three public datasets (Holidays, Ukbench and MIR Flickr 1 M as distractor). The experimental results are promising and outperform the state-of-the-art CBIR systems. In addition, only 117 bits are needed to represent each key-point which enables us to make our image retrieval schema suitable for large-scale experiments.