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Interpretable Image Recognition with Hierarchical Prototypes

arXiv.org Machine Learning

Vision models are interpretable when they classify objects on the basis of features that a person can directly understand. Recently, methods relying on visual feature prototypes have been developed for this purpose. However, in contrast to how humans categorize objects, these approaches have not yet made use of any taxonomical organization of class labels. With such an approach, for instance, we may see why a chimpanzee is classified as a chimpanzee, but not why it was considered to be a primate or even an animal. In this work we introduce a model that uses hierarchically organized prototypes to classify objects at every level in a predefined taxonomy. Hence, we may find distinct explanations for the prediction an image receives at each level of the taxonomy. The hierarchical prototypes enable the model to perform another important task: interpretably classifying images from previously unseen classes at the level of the taxonomy to which they correctly relate, e.g. classifying a hand gun as a weapon, when the only weapons in the training data are rifles. With a subset of ImageNet, we test our model against its counterpart black-box model on two tasks: 1) classification of data from familiar classes, and 2) classification of data from previously unseen classes at the appropriate level in the taxonomy. We find that our model performs approximately as well as its counterpart black-box model while allowing for each classification to be interpreted.


Compressively Sensed Image Recognition

arXiv.org Machine Learning

Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases.


Image Recognition: A peek into the future

#artificialintelligence

Our brains are wired in a way that they can differentiate between objects, both living and non-living by simply looking at them. In fact, the recognition of objects and a situation through visualization is the fastest way to gather, as well as to relate information. This becomes a pretty big deal for computers where a vast amount of data has to be stuffed into it, before the computer can perform an operation on its own. Ironically, with each passing day, it is becoming essential for machines to identify objects through facial recognition, so that humans can take the next big step towards a more scientifically advanced social mechanism. So, what progress have we really made in that respect?


Artificial Intelligence and Machine Learning in Medical Imaging

#artificialintelligence

The two major tasks in medical imaging that appear to be naturally predestined to be solved with AI algorithms are segmentation and classification. Most of techniques used in medical imaging were conventional image processing, or more widely formulated computer vision algorithms. One can find many works with artificial neural networks, the backbone of deep learning. However, most works were focused on conventional computer vision which focused, and still does, on "handcrafted" features, techniques that were the results of manual design to extract useful and differentiating information from medical images. Some progress was visible in the late 90s and early 2000s (for instance, the SIFT method in 1999, or visual dictionaries in early 2000s) but there were no breakthroughs.


Artificial Intelligence and Machine Learning in Medical Imaging

#artificialintelligence

The two major tasks in medical imaging that appear to be naturally predestined to be solved with AI algorithms are segmentation and classification. Most of techniques used in medical imaging were conventional image processing, or more widely formulated computer vision algorithms. One can find many works with artificial neural networks, the backbone of deep learning. However, most works were focused on conventional computer vision which focused, and still does, on "handcrafted" features, techniques that were the results of manual design to extract useful and differentiating information from medical images. Some progress was visible in the late 90s and early 2000s (for instance, the SIFT method in 1999, or visual dictionaries in early 2000s) but there were no breakthroughs.