Representation Learning Framework of Object Recognition via Feature Construction | ||||
| Mansoura Journal for Computer and Information Sciences | ||||
| Volume 14, Issue 1, June 2018, Page 37-42 PDF (666.09 K) | ||||
| Document Type: Original Research Articles. | ||||
| DOI: 10.21608/mjcis.2018.311995 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Muhammad H. Zayyan; Samir Elmougy; Mohammed F. Alrahmawy | ||||
| Faculty of computers and information systems , C.S dep. Mansoura University, Egypt | ||||
| Abstract | ||||
| In this paper, we recognize objects within images by collecting information from a large number of random-size patches of the image. The different backgrounds accompany the foreground object demand to have a learning system to identify each patch as belonging to the object category or to the background category. We strengthen a recent method called Evolution-COnstructed (ECO), which is based on the ensemble learning approach which combines several weak classifier. The improvement is relying on decreasing the overfitting problem. Two different improving ideas are proposed: 1) Pooling operation, which is applied to the weak classifiers data, 2) Random Forest algorithm, which combines the weak classifiers outcomes. Experimental results are reported for classification of 9 categories of Caltech-101 data sets and proved that our modifications boost the performance over the base method and other existing methods | ||||
| Keywords | ||||
| Object recognition; ECO features; Adaboost; Random Forest; Pooling; Genetic Algorithm | ||||
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