Fuzzy Aggregation Of Image Features In Content-Based Image Retrieval

The obstacle of generating hybrid queries within the context of content-based image retrieval is still very real. In attempts to overcome this, fuzzy aggregation can be used to combine single, simple index queries into larger, more complex ones. This paper outlines the use of a fuzzy aggregation technique for hybrid querying which has the ability to adjust its behavior according operator-controlled parameters. The resulting aggregator can be viewed as a featureadaptive overall similarity measure. For the purposes of this extended summary, the scope of the aggregator is limited to queries involving color content, color coverage, and horizontal /vertical trends, and applied to a media database comprised of COREL images of fixed size. Preliminary results show promise and illustrate that hybrid queries using the aforementioned fuzzy aggregator are effective in their ability to retrieve relevant images while suppressing erroneous retrievals when compared to simple, single-feature queries. In addition, the results obtained are at a minimum compara- ble to multiple-feature queries generated using a weighted mean approach but exhibiting scalability and greater fiexir bility in parameter adjustment