Name
Title | ||
|---|---|---|
Combining deep learning and unsupervised clustering to improve scene recognition performance |
Abstract | ||
|---|---|---|
Deep Neural Networks (DNN) are now the state-of-the-art for many image and object recognition tasks, as illustrated by their performance on standard benchmarks. The success of DNNs is attributed to their ability to learn rich mid-level image representations, as opposed to hand-designed low-level features used in other image analysis methods. Typically a large dataset of unlabeled images is used for unsupervised feature learning, and then standard classifiers are trained on the features extracted from the images in a labeled set. In this paper, we show that clustering the images using the features from the DNN allows more accurate per-cluster classifiers to be learned, which improves the overall classification accuracy. We demonstrate the effectiveness of our approach on a scene recognition task. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MMSP.2015.7340859 | 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) |
Keywords | Field | DocType |
deep learning,unsupervised clustering,scene recognition performance,deep neural networks,DNN,object recognition,mid-level image representations,unsupervised feature learning | Semi-supervised learning,Computer science,Feature (machine learning),Unsupervised learning,Artificial intelligence,Cluster analysis,Computer vision,Feature vector,Pattern recognition,Feature (computer vision),Feature extraction,Machine learning,Feature learning | Conference |
ISSN | Citations | PageRank |
2163-3517 | 0 | 0.34 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Armin Kappeler | 1 | 45 | 2.75 |
| Robin D. Morris | 2 | 114 | 14.64 |
| Amar Ramesh Kamat | 3 | 0 | 0.34 |
| N Rasiwasia | 4 | 1173 | 34.61 |
| Gaurav Aggarwal | 5 | 456 | 26.11 |