Abstract

Plenty of information is available today from different sources about a particular geographical area. However, none of it is reliable when different needs arise like for tourists, city planners, administrators, and lay people. We have designed a multimedia based city information system which attempts to solve this problem. In this paper we explain the design and implementation of our system. The paper discusses in detail the need for such a system, how it compares with similar existing systems and the features expected in such a system. It also discusses our implementation of the system and the future work we want to pursue in this direction

Abstract

Pixel correspondence is an important problem in stereo vision, motion, structure from motion, etc. Several procedures have been proposed in the literature for this problem, using a variety of image features to identify the corresponding features. Dierent features work wel l under dierent conditions. An algorithm that can seamlessly integrate multiple features in a flexible manner can combine the advantages of each. We propose a framework to combine heterogenous features, each with a dierent measure of importance, into a single correspondence computation in this paper. We also present an unsupervised procedure to select the optimal combination of features for a given pair of images by computing the relative importances of each feature. A unique aspect of our framework is that it is independent of the specic correspondence algorithm used. Optimal feature selection can be done using any correspondence mechanism that can be extended to use multiple features. We also present a few examples that demonstrate the eectiveness of the feature selection framework.

Abstract

Image databases index in them using features extracted from the image. The indexing scheme is decided apriori and is optimized for a specific querying criterion. This is not suitable for a generic database of images which may be queried by multiple users based on different criteria. IN this paper, we present a flexible scheme which adapts itself to the user 's preferences. Though the method uses a conservative set of features during indexing that includes a large number and type of fundamental features the query processing time does not increase due to this redundancy. A boot-strapping mechanism allows the user to build up a "desired class" from a few samples. The feature selection computation scales linearly with the size of the desired class, rather than that of the entire database. THis feature makes our algorithm viable for very large databases. We present an implementation of our scheme and some results from it.