Pursuing effective representation and efficient learning for multi-class image classification

Multi-class image classification is a fundamental problem in computer vision and multimedia research communities. The principal difficulties for successfully classifying different images come from two aspects: (1) Effectively representing images with the preservation of intra-class similarities and inter-class dissimilarities; (2) Efficiently learning a large amount of visual classes from example images based on their visual representations. The objective of the thesis is to develop algorithms that can automatically learn classifiers from human annotated example images and then accurately classify unseen images.For image representation, most of the existing approaches are based on local visual features which are extracted from small local regions of images, describing specific image characteristics, e.g., texture, shape, colour, and intensity. But local features are weak individually. In this thesis, appropriate combinations of either homogeneous or heterogeneous local features are exploited as visual patterns to bring more descriptive power for representing images. Furthermore, sharable informative visual pattern, which can be shared by different visual classes, is proposed for efficient multi-class image classification. Such compositional visual representation strategy is also explored on model level. Within multiple-instance learning framework, novel algorithms have been proposed for obtaining a more accurate and adaptive final representation model by combining a set of primitive models.For multi-class visual concepts learning, traditional approaches convert the problem into a collection of binary classification problems, each of which is tackled by a binary classification algorithm. So the scale of the problem rises at least linearly with the number of classes. And the efforts required on acquiring sufficient annotated training images also increase dramatically. In the thesis, a boosting-based multi-class classification algorithm is developed to ease such difficulties by utilising sharable informative visual patterns. And a novel batch mode active learning approach is also proposed with the consideration of informative correlations among classes. The proposed approaches have been justified experimentally on various public datasets. And significant improvements over the state-of-the-art techniques have been achieved