| dc.description.abstract | Recently visual place categorization is an important research topic due to its numerous potential applications. However, such visual categorization system is easily affected by object scale, illumination conditions, object occlusion and viewpoints. In addition, categorization system should be efficiently trained and tested with huge amount of visual cues extracted in a very short period. Relevant researches on visual place categorization rely on non-linear SVM to categorize those visual cues within each image, since non-linear SVM has always shown promising categorization results. However, its major defect is that it suffers from O(N2) to O(N3) in training complexity and O(D‧S) in test complexity, where N is the size of training data, D is the dimension of data vector, and S is the amount of support vector. Efficient training and test pro-cesses are demanding for tackling large-scale categorization problems. Therefore, this thesis proposes ensemble of local linear SVMs (ELL-SVMs), lowering training com-plexity to O(N1.5).
Our proposed scheme has training and test phases. In training phase, we propose a scheme for generating ensemble of local linear SVM (L-SVM). This idea is derived from discovering the linearly-separable partitions among training data, while such partitions are found, those partitions could be classified by linear SVM instead of non-linear one. In test phase, we impose nearest neighbour rule into Bayes decision rule to assist in identifying the best trained local L-SVM for test sample. Afterwards, we further propose confidence-based weighted one-against-all (CW-OAA) approach to fuse the categorization results of visual cues within an image, and thus to categorize the image. Empirically, the training speed of proposed ELL-SVMs is similar to that of FaLK-SVM and much lower than those of BVM and standard SVM. The test speed of ELL-SVMs is lower than FaLK-SVM, BVM and standard SVM. Moreover, the categorization ability of ELL-SVMs with CW-OAA outperforms three SVM’s variants with OAA.
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