7 research outputs found
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
This work addresses the problem of vehicle identification through
non-overlapping cameras. As our main contribution, we introduce a novel dataset
for vehicle identification, called Vehicle-Rear, that contains more than three
hours of high-resolution videos, with accurate information about the make,
model, color and year of nearly 3,000 vehicles, in addition to the position and
identification of their license plates. To explore our dataset we design a
two-stream CNN that simultaneously uses two of the most distinctive and
persistent features available: the vehicle's appearance and its license plate.
This is an attempt to tackle a major problem: false alarms caused by vehicles
with similar designs or by very close license plate identifiers. In the first
network stream, shape similarities are identified by a Siamese CNN that uses a
pair of low-resolution vehicle patches recorded by two different cameras. In
the second stream, we use a CNN for OCR to extract textual information,
confidence scores, and string similarities from a pair of high-resolution
license plate patches. Then, features from both streams are merged by a
sequence of fully connected layers for decision. In our experiments, we
compared the two-stream network against several well-known CNN architectures
using single or multiple vehicle features. The architectures, trained models,
and dataset are publicly available at https://github.com/icarofua/vehicle-rear
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition
Image recognition with prototypes is considered an interpretable alternative
for black box deep learning models. Classification depends on the extent to
which a test image "looks like" a prototype. However, perceptual similarity for
humans can be different from the similarity learned by the classification
model. Hence, only visualising prototypes can be insufficient for a user to
understand what a prototype exactly represents, and why the model considers a
prototype and an image to be similar. We address this ambiguity and argue that
prototypes should be explained. We improve interpretability by automatically
enhancing visual prototypes with textual quantitative information about visual
characteristics deemed important by the classification model. Specifically, our
method clarifies the meaning of a prototype by quantifying the influence of
colour hue, shape, texture, contrast and saturation and can generate both
global and local explanations. Because of the generality of our approach, it
can improve the interpretability of any similarity-based method for
prototypical image recognition. In our experiments, we apply our method to the
existing Prototypical Part Network (ProtoPNet). Our analysis confirms that the
global explanations are generalisable, and often correspond to the visually
perceptible properties of a prototype. Our explanations are especially relevant
for prototypes which might have been interpreted incorrectly otherwise. By
explaining such 'misleading' prototypes, we improve the interpretability and
simulatability of a prototype-based classification model. We also use our
method to check whether visually similar prototypes have similar explanations,
and are able to discover redundancy. Code is available at
https://github.com/M-Nauta/Explaining_Prototypes .Comment: 10 pages, 9 figure
Deep Learning Based Fine Grained Image Classification
Image classification, specifically object classification is the focused research area in the computer vision and machine learning field in the past decade. In image classification a label or category is assigned to an input image based on its content. With breakthroughs in deep learning-based approaches, performance of image classification models' has improved significantly, particularly fine-grained image classification, which includes discriminating between items of the same category with slight changes. The object classification can be categorised as coarse grained object classification, which identifies highly diverse object categories, such as an elephant and a bus. One example of this type of object classification is a bus and an elephant. On the other hand, fine-grained image categorization seeks to recognise photos as belonging to distinct species of animals, birds, or plants, as well as distinct models of automobiles, versions of aircraft, and so on. The purpose of this study is to evaluate previously published research that investigates deep learning techniques for the classification of fine-grained images and to compare the effectiveness of these techniques using datasets that are open to the public
On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator
Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise