Name
Abstract | ||
|---|---|---|
A number of applications varying from music to document classification, require the similarity between a collection of objects to be calculated. To achieve this, features about these objects are extracted e.g. keywords, shapes, colours, frequency components etc, to produce an N-dimensional feature vector, representing a point in a N-dimensional feature space. A database containing these feature vectors can be constructed, allowing query vectors to be applied and the distance between this vector and those stored in the database to be calculated. From the results of these comparisons, similar objects can now be identified and retrieved from the database for further processing by the application. There exists a number of commonly used distance or similarity measures e.g. city block, Euclidean, weighted cosine distance etc, with varying processing requirements and performance characteristics. This paper investigates the possibility of accelerating these distance measures by using FPGA based hardware IP cores and compares this to a software implementation based on a Sun Blade 2000 computer. |
Year | Venue | Keywords |
|---|---|---|
2005 | IADIS AC | ip core,weighted cosine,fpga.,euclidean,feature vector,feature space |
Field | DocType | Citations |
Data mining,Cosine Distance,Artificial intelligence,Euclidean geometry,City block,Computer hardware,Document classification,Feature vector,Pattern recognition,Existential quantification,Field-programmable gate array,Mathematics,Distance measures | Conference | 8 |
PageRank | References | Authors |
0.65 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Freeman | 1 | 36 | 4.03 |
| Michael Weeks | 2 | 130 | 16.29 |
| Jim Austin | 3 | 1167 | 66.82 |