Using Multiresolution Learning for Transfer in Image Classification
نویسندگان
چکیده
Our work explores the transfer of knowledge at multiple levels of abstraction to improve learning. By exploiting the similarities between objects at various levels of detail, multiresolution learning can facilitate transfer between image classification tasks. We extract features from images at multiple levels of resolution, then use these features to create models at different resolutions. Upon receiving a new task, the closest-matching stored model can be generalized (adapted to the appropriate resolution) and transferred to the new task.
منابع مشابه
Image Classification via Sparse Representation and Subspace Alignment
Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملAutomatic road crack detection and classification using image processing techniques, machine learning and integrated models in urban areas: A novel image binarization technique
The quality of the road pavement has always been one of the major concerns for governments around the world. Cracks in the asphalt are one of the most common road tensions that generally threaten the safety of roads and highways. In recent years, automated inspection methods such as image and video processing have been considered due to the high cost and error of manual metho...
متن کاملRegion Completion in a Texture using Multiresolution Transforms
Abstract Natural images, textures and photographs are likely to be impaired by stains. As a result a substantial portion of the image remains blurred. However, a method called region completion is adopted to fill in the tainted part by using the information from the portion left unblemished by stains. A novel method to perform this operation is proposed in this paper. The three significant sta...
متن کاملPorosity classification from thin sections using image analysis and neural networks including shallow and deep learning in Jahrum formation
The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted using the image analysis techniques. The extracted features and their corresponding pore types ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007