  In large-scale image retrieval, many indexing methods have been proposed to
narrow down the searching scope of retrieval. The features extracted from
images usually are of high dimensions or unfixed sizes due to the existence of
key points. Most of existing index structures suffer from the dimension curse,
the unfixed feature size and/or the loss of semantic similarity. In this paper
a new classification-based indexing structure, called Semantic Indexing
Structure (SIS), is proposed, in which we utilize the semantic categories
rather than clustering centers to create database partitions, such that the
proposed index SIS can be combined with feature extractors without the
restriction of dimensions. Besides, it is observed that the size of each
semantic partition is positively correlated with the semantic distribution of
database. Along this way, we found that when the partition number is normalized
to five, the proposed algorithm performed very well in all the tests. Compared
with state-of-the-art models, SIS achieves outstanding performance.
