Since media-based evaluation yields similarity values, results to a multimedia database query, Q(Y1, ..., Yn), is defined as an ordered list SQ of n-tuples of the form 〈X1, ..., Xn〉. The query Q itself is composed of a set of fuzzy and crisp predicates, constants, variables, and conjunction, disjunction, and negation operators. Since many multimedia applications require partial matches, SQ includes results which do not satisfy all predicates. Due to the ranking and partial match requirements, traditional query processing techniques do not apply to multimedia databases. In this paper, we first focus on the problem of `given a multimedia query which consists of multiple fuzzy and crisp predicates, providing the user with a meaningful final ranking'. More specifically, we study the problem of merging similarity values in queries with multiple fuzzy predicates. We describe the essential multimedia retrieval semantics, compare these with the known approaches, and propose a semantics which captures the requirements of multimedia retrieval problem. We then build on these results in answering the related problem of `given a multimedia query which consists of multiple fuzzy and crisp predicates, finding an efficient way to process the query.' We develop an algorithm to efficiently process queries with unordered fuzzy predicates (sub-queries). Although this algorithm can work with different fuzzy semantics, it benefits from the statistical properties of the semantics proposed in this paper. We also present experimental results for evaluating the proposed algorithm in terms of quality of results and search space reduction.
ASJC Scopus subject areas
- Information Systems and Management