Stable multi-label boosting for image annotation with structural feature selection
نویسندگان
چکیده
منابع مشابه
MLIFT: Enhancing Multi-label Classifier with Ensemble Feature Selection
Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...
متن کاملImproved Image Annotation and Labelling through Multi-Label Boosting
The majority of machine learning systems for object recognition is limited by their requirement of single labelled images for training, which are difficult to create or obtain in quantity. It is therefore impractical to use methods or techniques which require such data to build object recognizers for more than a relatively small subset of object classes. Instead, far more abundant multilabel da...
متن کاملMulti-Modal Image Annotation with Multi-Label Multi-Instance LDA
This paper studies the problem of image annotation in a multi-modal setting where both visual and textual information are available. We propose Multimodal Multi-instance Multi-label Latent Dirichlet Allocation (M3LDA), where the model consists of a visual-label part, a textual-label part and a labeltopic part. The basic idea is that the topic decided by the visual information and the topic deci...
متن کاملFeature Selection for Automatic Image Annotation
Automatic image annotation empowers the user to search an image database using keywords, which is often a more practical option than a query-by-example approach. In this work, we present a novel image annotation scheme which is fast and effective and scales well to a large number of keywords. We first provide a feature weighting scheme suitable for image annotation, and then an annotation model...
متن کاملMulti-Label Informed Feature Selection
Multi-label learning has been extensively studied in the area of bioinformatics, information retrieval, multimedia annotation, etc. In multi-label learning, each instance is associated with multiple interdependent class labels, the label information can be noisy and incomplete. In addition, multi-labeled data often has high-dimensional noisy, irrelevant and redundant features. As an effective d...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Science China Information Sciences
سال: 2011
ISSN: 1674-733X,1869-1919
DOI: 10.1007/s11432-011-4483-5