By Christos Faloutsos (auth.)
Searching Multimedia Databases via Content bridges the space among the database and sign processing groups by way of offering the required historical past info for the reader and offering it in addition to the instinct and mechanics of the easiest current instruments in every one zone.
the 1st half Searching Multimedia Databases by means of Content stories the main profitable database entry tools, in expanding complexity, achieving as much as spatial entry equipment and textual content retrieval. In all instances, the emphasis is on useful ways which have been included in advertisement structures, or that appear very promising.
the second one half the booklet makes use of the above entry the way to in achieving speedy looking out in a database of indications. A normal technique is gifted, which means extracting a number of solid gains from each one multimedia item, hence mapping items into issues in a metric house. ultimately, the e-book concludes by means of featuring a few contemporary profitable functions of the method on time sequence and colour photographs.
Searching Multimedia Databases via Content is concentrated in the direction of researchers and builders of multimedia structures. The e-book may also function a textbook for a graduate path on multimedia looking out, protecting either entry tools in addition to the fundamentals of sign processing.
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1 Illustration of superimposed coding; F=12 bits for a signature, with m=4 bits per word set to 1. On searching for a word, say' data', we create its word signature, and exclude all the document. signatures that do not have a '1' at the corresponding bit positions. The choice of the signature length F depends on the desirable falsedrop rate; the m parameter is chosen such that, on the average, half of the bits should be '1' in a document signature [Sti60]. The method has been studied and used extensively in text retrieval: on bibliographic entries [FH69]; for fast substring matching [Har71] [KR87]; in office automation and message filing [TC83] [FC87].
With grid files [HN83], B-trees [FR91]' hB-trees [L890j as the underlying PAM. The weak points are the following: (a) the parameter space has high dimensionality, inviting 'dimensionality curse' problems earlier on (see the discussion on page 39). (b) except for range queries, there are no published algorithms for nearest-neighbor and spatial join queries. Nevertheless, it is a clever idea, which can be valuable for a stand-alone, special purpose system, operating on a low-dimensionality address space.
This way, the R-tree can guarantee good space utilization and remain balanced. 6(b) shows the file structure for the same R-tree, where nodes correspond to disk pages. The R-tree inspired much subsequent work, whose main focus was to improve the search time. A packing technique is proposed in [RL85j to minimize the overlap between different nodes in the R-tree for static data. The idea was to order the data in, say, ascending x-low value, and scan the list, filling each leaf node to capacity. 7(b) shows the resulting R-tree parents according to the x-low sorting.