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Fruit flies are of huge biological and economic importance for the farming of different countries in the World, especially for Brazil. Brazil is the third largest fruit producer in the world with 44 million tons in 2016. The direct and indirect losses caused by fruit flies can exceed USD 2 billion, putting these pests as one of the biggest problems of the world agriculture. In Brazil, it is estimated...
Image classification is a method that distinguishes the different categories of targets based on the different features of image. The current problem usually is that the feature modeling of target has a great influence on recognition robustness. In order to solve this problem, a correlation-based method is presented to optimize the bag-of-visual-word (BOVW) model by reducing the dictionary size. The...
Deep learning has brought a series of breakthroughs in image processing. Specifically, there are significant improvements in the application of food image classification using deep learning techniques. However, very little work has been studied for the classification of food ingredients. Therefore, this paper proposes a new framework, called DeepFood which not only extracts rich and effective features...
Image classification algorithms using state-of-the art methods have grabbed much attention in computer vision area. In-domain classification assumes the testing data to be in the same domain as of the training data. Cross-Domain classification is a paradigm where testing data is from a different but related domain to the training data. We use Speeded-Up Robust Features (SURF) for feature extraction,...
With the exponential growth of web meta-data, exploiting multimodal online sources via standard search engine has become a trend in visual recognition as it effectively alleviates the shortage of training data. However, the web meta-data such as text data is usually not as cooperative as expected due to its unstructured nature. To address this problem, this paper investigates the numerical representation...
Nowadays, the “semantic gap” problems have greatly limited development of image classification. The key to this problem is to get semantic information of the images. A semantic image feature extraction method is proposed in this paper, in which eye movement information is integrated. Firstly, the underlying visual features of images are extracted. Secondly, weighed feature vectors of images are constructed...
Human action recognition is an imperative research area in the field of computer vision due to its numerous applications. Recently, with the emergence and successful deployment of deep learning techniques for image classification, object recognition, and speech recognition, more research is directed from traditional handcrafted to deep learning techniques. This paper presents a novel method for human...
Images on the Internet and in multimedia systems are rising successively. There are different research works on visual information and automatic analysis of images. Image memorability is a new task in computer vision. Actually, the human brain processes simultaneously millions of images and other information from multiple sources. Among these various images and information some of them are more memorable...
To achieve more effective solution for large-scale image classification (i.e., classifying millions of images into thousands or even tens of thousands of object classes or categories), a deep multi-task learning algorithm is developed by seamlessly integrating deep CNNs with multi-task learning over the concept ontology, where the concept ontology is used to organize large numbers of object classes...
A novel Hierarchical Structured Dictionary Learning (HSDL) algorithm is proposed in this paper. It aims to learn classs-pecific dictionaries for all classes simultaneously in a hierarchical structure. A discriminative term based on Fisher discrimination criterion is jointly considered for both the classs-pecific dictionaries in the lower level and the shared dictionaries in the upper level to enhance...
In this paper we propose to face the problem of event detection from single images, by exploiting both background information often containing revealing contextual clues and details, which are salient for recognizing the event. Such details are visual objects critical to understand the underlying event depicted in the image and were recently defined in the literature as “event-saliency”. Adopting...
In this work, an interactive visual system MICS is presented for large-scale brain CT image classification. Automatic feature extraction algorithms are added in MICS to improve system efficiency and classification accuracy. In visualization part, we designed an interactive feature extraction interface, enable users to extract and fine-tune image features according to specific requirements. In addition,...
Figure detection, separation and image classification are common problems occurring in various fields, especially medicine. Since image databases are usually large, manual classification would be a demanding task. In this paper, we proposed a method for automatic compound figure detection and separation, and gave a comparison between other recognition methods, such as convolutional neural networks...
The social insect metaphor for solving problems has become an emerging topic in the recent years emphasizing on stochastic construction practice, building the key probabilistically to optimize the solution related to any kind of a problem. As we are aware that image makes the human visualize the existence of entities in nature and helps individual to get the feel of the services without solely relying...
In this paper, we address the problem of natural flower classification. It is a challenging task due to the non-rigid deformation, illumination changes, and inter-class similarity. We build a large dataset of flower images in the wide with 79 categories and propose a novel framework based on convolutional neural network (CNN) to solve this problem. Unlike other methods using hand-crafted visual features,...
Sparse Coding is a widely used method to represent an image. However, sparse coding and its improved algorithms have the problem of complex computation and long running time and so on. For these problems, we propose an image classification method based on hash codes and space pyramid, which encodes local feature points with hash codes instead of sparse coding. Firstly, extract the local feature points...
Computational visual atention models aims to emulate the Human Visual System performance in selecting relevant features for efficient visual scene processing. As a result, visual saliency maps highlights relevant visual patterns in an image, possibly associated with objects or specific concepts. In the analysis of medical images, this allows the radiologist or clinical expert to focus the attention...
Image emotion analysis is a new and challenging research direction that gains more and more attention in the research community. Most previous works in this field only use common or generic features, and have hard restrictions on training images, such as scale, resolution, etc. Inspired by scale-space theories and psychology theories of color, we propose a procedure to extract interpretive features...
We consider the use of transfer learning, via the use of deep Convolutional Neural Networks (CNN) for the image classification problem posed within the context of X-ray baggage security screening. The use of a deep multi-layer CNN approach, traditionally requires large amounts of training data, in order to facilitate construction of a complex complete end-to-end feature extraction, representation...
In complex visual recognition systems, feature fusion has become crucial to discriminate between a large number of classes. In particular, fusing high-level context information with image appearance models can be effective in object/scene recognition. To this end, we develop an auto-context modeling approach under the RKHS (Reproducing Kernel Hilbert Space) setting, wherein a series of supervised...
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