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Cruz-Roa, A., Arevalo, J., Madabhushi, A. & González, F. A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection.
Considering the sparse property of images, we utilize the bag-of-words (BoW) model for image representation and propose a novel BIQA metric.
Models for image representation generally involve minimum distances between the pixels and borders of components, pixels closer to a given set of pixels than to another set.
In this paper, a new learning based mechanism is proposed to learn invariant image features that are optimal for image representation in a data-driven way.
The VLAD approach is designed as a method with high efficiency in terms of time and memory consumption, which accumulates local features as a compacted low-dimensional descriptor for image representation.
Then, we develop a new Convolutional Neural Network (CNN), called DiNet, that uses more than two pre-trained convolutional layers to consider local to global features for image representation.
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Similarly, if we formulate the Eq. (6) based on LTP instead of WLTP, the index of the rotation invariant co-occurrence between two LTP pairs (RICLTP) can be formed and then the statistics (i.e., histogram) of the RICLTP can be used for image representations.
Hence, in order to obtain completed features, all feature maps for a convolutional layer should be considered for the image representation.
"Multi-Scale Hybrid Linear Models for Lossy Image Representation," Microsoft Research in Asia, Beijing, China, May 18 , 2005
"Multi-Scale Hybrid Linear Models for Lossy Image Representation," Robotics Lab Seminar, EECS Department, University of California at Berkeley, June 14 , 2005
"Multi-Scale Hybrid Linear Models for Lossy Image Representation," Automation Department Seminar, Tsinghua University, Beijing, China, May 19 , 2005
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