Showing papers on "Feature hashing published in 1994"

Using tries to eliminate pattern collisions in perfect hashing

Abstract: Many current perfect hashing algorithms suffer from the problem of pattern collisions. In this paper, a perfect hashing technique that uses array-based tries and a simple sparse matrix packing algorithm is introduced. This technique eliminates all pattern collisions, and, because of this, it can be used to form ordered minimal perfect hashing functions on extremely large word lists. This algorithm is superior to other known perfect hashing functions for large word lists in terms of function building efficiency, pattern collision avoidance, and retrieval function complexity. It has been successfully used to form an ordered minimal perfect hashing function for the entire 24481 element Unix word list without resorting to segmentation. The item lists addressed by the perfect hashing function formed can be ordered in any manner, including alphabetically, to easily allow other forms of access to the same list. >

Interactive learning of multiple attribute hash table for fast 3D object recognition

Abstract: Multiple-attribute hashing is now considered to be a powerful approach for the recognition and localization of 3D objects on the basis of their invariant properties. In the systems developed to date, the hash tables must be created by the system developer/spl minus/an onerous task especially when the number of attributes is large, which is the case in systems that use both geometric and nongeometric attributes. The authors show how the tools of decision trees can be used for the automatic construction of hash tables. Their decision tree framework is based on a hybrid method that uses both qualitative attributes, such as the shape of a surface, and quantitative attributes such as color, dihedral angles, etc. In the system proposed the system developer shows objects to a vision system and, in an interactive mode, tells the system the model identities of the various segmented regions, etc. Subsequently, the decision tree based framework learns the structure of the hash table. >

Performance Improvement of State Space Exploration by Regular & Diffrential Hashing Functions

Abstract: This paper presents regular hashing functions. Used in conjunction with differential computation process, regular hashing functions enable searching time of global state to be optimized. After a formal definition of the regular property for hashing functions, we propose a characterization of this property. Then the formal definition of differential hashing function is given. Next, we show the performance acceleration produced by the precomputed differential computation process applied to three hashing functions commonly used. The observed accelerations can be significant because the complexity of proposed implementation is independent of key length or respectively of item difference contrary to the usual or respectively differential implementations. Last we study the performances of precomputed differential hashing computation process on reachability graph exploration of distributed systems specified by Petri net using the Bouster tool, and on state space exploration of protocols specified by Lotos using the Open/Caesar environment.

Guaranteed geometric hashing

Abstract: Geometric hashing is an invariant feature-driven approach to model-based object recognition. Previous interest has focused on its ability to accommodate sensor error. This paper presents an enhancement of the geometric hashing technique which guarantees, under only a few constraints, that models will not be missed due to sensor noise. The authors' geometric hashing algorithm enters model affine invariants into hash table regions defined by an exact error model, brings together known optimizations (table symmetry and the use of more than 3 model-scene point correspondences) and uses novel data organization. Experimental results (on both synthetic and real data) suggest that the authors' modifications to a geometric hashing recognition scheme effectively overcome sensor noise.