Name
Abstract | ||
|---|---|---|
Reliable object detection is very important in computer vision and robotics applications. The histogram of oriented gradients (HOG) is established as one of the most popular hand-crafted features, which along with support vector machine (SVM) classification provides excellent performance for object recognition. We investigate dimensionality deduction on HOG features in combination with SVM classifiers to obtain efficient feature representation and improved classification performance. In addition to lean HOG features, we explore descriptors resulting from dimensionality reduction on histograms of binary descriptors. We consider three-dimensionality reduction techniques: standard principal component analysis, random projections, a computationally efficient linear mapping that is data independent, and locality preserving projections (LPP), which learns the manifold structure of the data. Our methods focus on the application of eye detection and were tested on an eye database created using the BioID and FERET face databases. Our results indicate that manifold learning is beneficial to classification utilizing HOG features. To demonstrate the broader usefulness of lean HOG features for object class recognition, we evaluated our system's classification performance on the CalTech-101 dataset with favorable outcomes. (C) 2015 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/1.JEI.24.6.063007 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
eye detection,histogram of oriented gradients,principal component analysis,random projections,manifold learning,locality preserving projections | Computer vision,Object detection,Histogram,Dimensionality reduction,Pattern recognition,Computer science,Support vector machine,Histogram of oriented gradients,Artificial intelligence,Nonlinear dimensionality reduction,Principal component analysis,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
24 | 6 | 1017-9909 |
Citations | PageRank | References |
4 | 0.45 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| riti sharma | 1 | 4 | 0.45 |
| Andreas Savakis | 2 | 377 | 41.10 |