Semi-Supervised Sparse Coding

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets

Multi-label learning by Image-to-Class distance for scene classification and image annotation

In multi-label learning, an image containing multiple objects can be assigned to multiple labels, which makes it more chal-lenging than traditional multi-class classification task where an image is assigned to only one label. In this paper, we propose a multi-label learning framework based on Image-to-Class (I2C) distance, which is recently shown useful for image classification. We adjust this I2C distance to cater for the multi-label problem by learning a weight attached to each local feature patch and formulating it into a large margin optimization problem. For each image, we constrain its weighted I2C distance to the relevant class to be much less than its distance to other irrelevant class, by the use of a margin in the optimization problem. Label ranks are generated under this learned I2C distance framework for a query image. Thereafter, we employ the label correlation in-formation to split the label rank for predicting the label(s) of this query image. The proposed method is evaluated in the applications of scene classification and automatic image annotation using both the natural scene dataset and Mi-crosoft Research Cambridge (MSRC) dataset. Experiment results show better performance of our method compared to previous multi-label learning algorithms

情報検索における意味的ギャップの解消 : トピックモデルを用いた先進的画像探索

Tohoku University徳山豪課

A picture is worth a thousand words : content-based image retrieval techniques

In my dissertation I investigate techniques for improving the state of the art in content-based image retrieval. To place my work into context, I highlight the current trends and challenges in my field by analyzing over 200 recent articles. Next, I propose a novel paradigm called __artificial imagination__, which gives the retrieval system the power to imagine and think along with the user in terms of what she is looking for. I then introduce a new user interface for visualizing and exploring image collections, empowering the user to navigate large collections based on her own needs and preferences, while simultaneously providing her with an accurate sense of what the database has to offer. In the later chapters I present work dealing with millions of images and focus in particular on high-performance techniques that minimize memory and computational use for both near-duplicate image detection and web search. Finally, I show early work on a scene completion-based image retrieval engine, which synthesizes realistic imagery that matches what the user has in mind.LEI Universiteit LeidenNWOImagin