12,282 research outputs found
Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Given a food image, can a fine-grained object recognition engine tell "which
restaurant which dish" the food belongs to? Such ultra-fine grained image
recognition is the key for many applications like search by images, but it is
very challenging because it needs to discern subtle difference between classes
while dealing with the scarcity of training data. Fortunately, the ultra-fine
granularity naturally brings rich relationships among object classes. This
paper proposes a novel approach to exploit the rich relationships through
bipartite-graph labels (BGL). We show how to model BGL in an overall
convolutional neural networks and the resulting system can be optimized through
back-propagation. We also show that it is computationally efficient in
inference thanks to the bipartite structure. To facilitate the study, we
construct a new food benchmark dataset, which consists of 37,885 food images
collected from 6 restaurants and totally 975 menus. Experimental results on
this new food and three other datasets demonstrates BGL advances previous works
in fine-grained object recognition. An online demo is available at
http://www.f-zhou.com/fg_demo/
Convolutional neural network model in machine learning methods and computer vision for image recognition: a review
Recently, Convolutional Neural Networks (CNNs) are used in variety of areas including image and pattern recognition, speech recognition, biometric embedded vision, food recognition and video analysis for surveillance, industrial robots and autonomous cars. There are a number of reasons that convolutional neural networks (CNNs) are becoming important. Feature extractors are hand designed during traditional models for image recognition. In CNNs, the weights of the convolutional layer being used for feature extraction in addition to the fully connected layer are applied for classification that are determined during the training process. The objective of this paper is to review a few learning machine methods of convolutional neural network (CNNs) in image recognition. Furthermore, current approaches to image recognition make essential use of machine learning methods. Based on twenty five journal that have been review, this paper focusing on the development trend of convolution neural network (CNNs) model due to various learning method in image recognition since 2000s, which is mainly introduced from the aspects of capturing, verification and clustering. Consequently, deep convolutional neural network (DCNNs) have shown much successful in various machine learning and computer
vision problem because it significant quality gain at a modest increase of computational requirement. This training method also allows models that are composed of multiple processing layers to learn representation of data with multiple levels of abstraction
Lemon Classification Using Deep Learning
Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of
many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing
demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate
modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon
classification approach is presented with a dataset that contains approximately 2,000 images belong to 3 species at a few
developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to
image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used
in farming automation have the latent to enhance crop harvest and improve output and productivity when designed
properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this
approach
FoodNet: Recognizing Foods Using Ensemble of Deep Networks
In this work we propose a methodology for an automatic food classification
system which recognizes the contents of the meal from the images of the food.
We developed a multi-layered deep convolutional neural network (CNN)
architecture that takes advantages of the features from other deep networks and
improves the efficiency. Numerous classical handcrafted features and approaches
are explored, among which CNNs are chosen as the best performing features.
Networks are trained and fine-tuned using preprocessed images and the filter
outputs are fused to achieve higher accuracy. Experimental results on the
largest real-world food recognition database ETH Food-101 and newly contributed
Indian food image database demonstrate the effectiveness of the proposed
methodology as compared to many other benchmark deep learned CNN frameworks.Comment: 5 pages, 3 figures, 3 tables, IEEE Signal Processing Letter
A deep representation for depth images from synthetic data
Convolutional Neural Networks (CNNs) trained on large scale RGB databases
have become the secret sauce in the majority of recent approaches for object
categorization from RGB-D data. Thanks to colorization techniques, these
methods exploit the filters learned from 2D images to extract meaningful
representations in 2.5D. Still, the perceptual signature of these two kind of
images is very different, with the first usually strongly characterized by
textures, and the second mostly by silhouettes of objects. Ideally, one would
like to have two CNNs, one for RGB and one for depth, each trained on a
suitable data collection, able to capture the perceptual properties of each
channel for the task at hand. This has not been possible so far, due to the
lack of a suitable depth database. This paper addresses this issue, proposing
to opt for synthetically generated images rather than collecting by hand a 2.5D
large scale database. While being clearly a proxy for real data, synthetic
images allow to trade quality for quantity, making it possible to generate a
virtually infinite amount of data. We show that the filters learned from such
data collection, using the very same architecture typically used on visual
data, learns very different filters, resulting in depth features (a) able to
better characterize the different facets of depth images, and (b) complementary
with respect to those derived from CNNs pre-trained on 2D datasets. Experiments
on two publicly available databases show the power of our approach
- …