50
edits
Changes
→Piecing Together the Segmentation Jigsaw using Context
We present an approach to jointly solve the segmentation and recognition problem using a multiple segmentation framework. We formulate the problem as segment selection from a pool of segments, assigning each selected segment a class label. Previous multiple segmentation approaches used local appearance matching to select segments in a greedy manner. In contrast, our approach formulates a cost function based on contextual information in conjunction with appearance matching. This relaxed cost function formulation is minimized using an efficient quadratic programming solver and an approximate solution is obtained by discretizing the relaxed solution. Our approach improves labeling performance compared to other segmentation based recognition approaches.
We present an approach to jointly solve the segmentation and recognition problem using a multiple segmentation framework. We formulate the problem as segment selection from a pool of segments, assigning each selected segment a class label. Previous multiple segmentation approaches used local appearance matching to select segments in a greedy manner. In contrast, our approach formulates a cost function based on contextual information in conjunction with appearance matching. This relaxed cost function formulation is minimized using an efficient quadratic programming solver and an approximate solution is obtained by discretizing the relaxed solution. Our approach improves labeling performance compared to other segmentation based recognition approaches.
===Discriminative Dictionary Learning for Sparse Coding : A Batch Version and a Semi-Supervised Online Version===
Speaker: Guangxiao Zhang -- Date: February 21, 2013
Dictionary learning has been a hot topic in recent years in computer vision. Many dictionary learning strategies have been proposed and led to excellent results in image denoising, inpainting, and recognition. However, most of them optimizes for reconstruction and therefore may not be the best for classification. Moreover, such dictionaries are often over-complete (more dictionary items than the dimension), which is computationally costly when the number of categories is large.
In the first part of the talk, we present a greedy learning algorithm to obtain a compact (small-sized) and discriminative dictionary. Starting with an over-complete dictionary, we map the dictionary items with label into an undirected k-nearest neighbor graph, and model the discriminative dictionary learning as a graph topology selection problem. By optimizing a monotonic, submodular objective function, our algorithm is shown to be highly efficient and effective in face recognition, object recognition, and action/gesture classification tasks.
In the second part of the talk, we present an online, semi-supervised dictionary learning algorithm that is suitable when the size of the dataset is large and the labels are expensive to obtain. Similar to the previous work, our goal is to obtain a dictionary which is both representative and discriminative. Besides learning from labeled data, we also exploit the large amount of cheap, unlabeled training data to reinforce the representation power. An online framework makes the algorithm applicable to large-scale dataset.
==Past Semesters==
==Past Semesters==