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Multi-instance multi-label learning for image classification with large vocabularies.
| Content Provider | CiteSeerX |
|---|---|
| Author | Yakhnenko, Oksana Honavar, Vasant |
| Abstract | Multiple Instance Multiple Label learning problem has received much attention in machine learning and computer vision literature due to its applications in image classification and object detection. However, the current state-of-the-art solutions to this problem lack scalability and cannot be applied to datasets with a large number of instances and a large number of labels. In this paper we present a novel learning algorithm for Multiple Instance Multiple Label learning that is scalable for large datasets and performs comparable to the state-of-the-art algorithms. The proposed algorithm trains a set of discriminative multiple instance classifiers (one for each label in the vocabulary of all possible labels) and models the correlations among labels by finding a low rank weight matrix thus forcing the classifiers to share weights. This algorithm is a linear model unlike the state-of-the-art kernel methods which need to compute the kernel matrix. The model parameters are efficiently learned by solving an unconstrained optimization problem for which Stochastic Gradient Descent can be used to avoid storing all the data in memory. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Image Classification Large Vocabulary Multi-instance Multi-label Learning Large Number State-of-the-art Kernel Method Kernel Matrix Unconstrained Optimization Problem Object Detection Share Weight Current State-of-the-art Solution Large Datasets Possible Label Model Parameter Much Attention State-of-the-art Algorithm Linear Model Problem Lack Scalability Computer Vision Literature Low Rank Weight Matrix Novel Learning Algorithm Stochastic Gradient Descent Machine Learning Discriminative Multiple Instance Classifier Multiple Instance Multiple Label Learning |
| Content Type | Text |
| Resource Type | Article |
