A Class Imbalance Loss for Imbalanced Object Recognition
نویسندگان
چکیده
منابع مشابه
Fast Object Class Recognition
We propose a novel approach for object class recognition using scale invariant features and Gaussian Processes as our kernel-based classifier. We measure the performance of this approach in two stages: predicting the presence of a class of objects in images and localizing them. Our object class recognition method is comparable to other state-of-the-art approaches. Furthermore, we propose sophis...
متن کاملVisual Object Class Recognition
This dissertation implements, compares and evaluates different methods that can used to make inference about the existence of a specific object class in images. Initially, a visual vocabulary is created from the training data. Afterwards, the image content is expressed into an image descriptor using this visual vocabulary. Finally, different classification methods are used to make inference abo...
متن کاملa computational visual neuroscience model for object recognition
in this study with the inspirations from both neuroscience and computer science, a combinatorial framework for object recognition was proposed having benefited from the advantages of both biologically-inspired hmax_s architecture model for feature extraction and extreme learning machine (elm) as a classifier. hmax model is a feed-forward hierarchical structure resembling the ventral pathway in ...
متن کاملAddressing Class Imbalance for Improved Recognition of Implicit Discourse Relations
In this paper we address the problem of skewed class distribution in implicit discourse relation recognition. We examine the performance of classifiers for both binary classification predicting if a particular relation holds or not and for multi-class prediction. We review prior work to point out that the problem has been addressed differently for the binary and multi-class problems. We demonst...
متن کاملTrainable Visual Models for Object Class Recognition
Recognizing object classes, such as cars, planes or elephants, in an image or a video remains one of the most challenging problems in Computer Vision. However, recently a number of successes have been achieved by using ideas and algorithms from statistical learning theory, where visual models are trained using positive and negative examples of the class.
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: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
سال: 2020
ISSN: 1939-1404,2151-1535
DOI: 10.1109/jstars.2020.2995703