Multi-Index Multi-Object Content-Based Retrieval

作者：Klaric Matthew N^*; Scott Grant J; Shyu Chi Ren

来源：IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(10): 4036-4049.

DOI：10.1109/TGRS.2012.2187353

• Content-based retrieval (CBR)
• geographic information systems
• image databases
• object indexing
• search methods

In many large-scale content-based retrieval (CBR) applications, the input to the search process is a complex query that may be composed of several constituent parts. The proposed approach performs CBR queries by breaking down a complex query into several smaller heterogeneous queries. Object-based queries in an imagery search application can be performed by executing a search over several distinct feature space indexes. For example, CBR indexes may exist for spectral, texture, and shape feature vectors extracted from objects. A query for similar objects can be completed by aggregating the results from these multiple indexes. Complementing this concept, a multi-object search can be used to identify relevant groups of objects which match a given set of query objects. For example, a set of objects identified in satellite imagery could be used as a CBR query in order to identify similar groups of objects. Thus, a query can be performed for each object, and these results can be aggregated into multi-object search results by determining the optimal match of the query objects to those in each resulting group. We introduce the absence penalty method and obligatory object query algorithms for performing multi-index and multi-object CBR searches and provide experimental results that show that the proposed approaches efficiently provide search results with a high degree of precision with minimal error. The experimental results shown demonstrate the efficiency and accuracy of the proposed methods; moreover, through the fusion of multi-index and multi-object search techniques, we are able to construct new sophisticated query mechanisms.

• 出版日期2012-10