We present an image spot query technique as an alternative for content-based image retrieval based on similarity over feature vectors. Image spots are selective parts of a query image designated by users as highly relevant for the desired answer set. Compared to traditional approaches, our technique allows users to search image databases for local (spatial, color and color transition) characteristics rather than global features.
When a user query is presented to our search engine, the engine does not impose any (similarity, ranking, cutoff) policy of its own on the answer set; it performs an exact match based on the query terms against the database. Semantic higher concepts such as weighing the relevance of query terms, is left to the user as a task while refining their query to reach the desired answer set. Given the hundreds of feature terms involved in query spots, refinement algorithms are to be encapsulated in separate applications, which act as an intermediary between our search engine and the users
