Improving object detection with boosted histograms
نویسندگان
چکیده
منابع مشابه
Improving object detection with boosted histograms
We address the problem of visual object class recognition and localization in natural images. Building upon recent progress in the field we show how histogram-based image descriptors can be combined with a boosting classifier to provide a state of the art object detector. Among the improvements we introduce a weak learner for multi-valued histogram features and show how to overcome problems of ...
متن کاملImprovements of Object Detection Using Boosted Histograms
We present a method for object detection that combines AdaBoost learning with local histogram features. On the side of learning we improve the performance by designing a weak learner for multi-valued features based on Weighted Fisher Linear Discriminant. Evaluation on the recent benchmark for object detection confirms the superior performance of our method compared to the state-of-the-art. In p...
متن کاملEfficient Boosted Weak Classifiers for Object Detection
This paper accelerates boosted nonlinear weak classifiers in boosting framework for object detection. Although conventional nonlinear classifiers are usually more powerful than linear ones, few existing methods integrate them into boosting framework as weak classifiers owing to the highly computational cost. To address this problem, this paper proposes a novel nonlinear weak classifier named Pa...
متن کاملBoosted Fractal Integral Paths for Object Detection
In boosting-based object detectors, weak classifiers are often build on Haar-like features using conventional integral images. That approach leads to the utilization of simple rectangle-shaped structures which are only partial suitable for curved-shaped structures, as present in natural object classes such as faces. In this paper, we propose a new class of fractal features based on space-fillin...
متن کاملObject detection using boosted local binaries
This paper presents a novel binary descriptor Boosted Local Binary (BLB) for object detection. The proposed descriptor encodes variable local neighbour regions in different scales and locations. Each region pair of the proposed descriptor is selected by the RealAdaBoost algorithm with a penalty term on the structural diversity. As a result, confident features that are good at describing specifi...
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: Image and Vision Computing
سال: 2009
ISSN: 0262-8856
DOI: 10.1016/j.imavis.2008.08.010