A Semantic Indexing Structure for Image Retrieval
This addresses indexing challenges in image retrieval for applications like search engines, though it appears incremental as it builds on existing classification-based methods.
The paper tackles the problem of high-dimensional or variable-sized features in large-scale image retrieval by proposing a Semantic Indexing Structure (SIS) that uses semantic categories for database partitions, achieving outstanding performance compared to state-of-the-art models with a normalized partition number of five.
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.