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Various statistical methods such as co-occurrence matrix, local binary patterns and spectral approaches such as Gabor filters have been used for generating global features for image classification. However, global image features fail to distinguish between local variations within an image. Bag-of-visual-words (BoVW) model do capture local variations in an image, but typically do not consider spatial...
To train a scene classifier with good generalization capability, a large number of human labeled training images are often needed. However, a large number of well-labeled training images may not always be available. To alleviate this problem, the web resources-aided scene classification framework was proposed. The present paper is a new development based on our previously proposed framework [1], with...
Deep convolutional neural networks is a recently developed method that yields very successful results in image classification. Deep neural networks, which have a high number of parameters, require a large amount of data to avoid overfitting during training. For applications in which the available data is not adequate to train a deep neural network from the scratch, deep neural networks trained for...
Deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition. In this study, we investigate various aspects of convolutional neural networks (CNNs) from the big data perspective. We analyze recent studies and different network architectures both in terms of running time and accuracy...
In the study on sports image classification, the characteristics of human pose increasingly raise concerns of researchers. However, the same posture for human may be resulted from different scenes and scene objects that express diverse action states and meanings. Thus, combination of human pose and event scenes shall be considered so as to improve performance of sports image classification. In recent...
In image classification and retrieval, the semantic gap is the major challenge. It characterizes the difference between human perception of a concept and how it can be represented using machine level language. Bag of visual words is a well-known efficient method for image representation, however it showed some limitations. The loss of information during the vector quantization process is one of these...
We present an image classification method which consists of salient region (SR) detection, local feature extraction, and pairwise local observations based Naive Bayes classifier (NBPLO). Different from previous image classification algorithms, we propose a scale, translation, and rotation invariant image classification algorithm. Based on the discriminative pairwise local observations, we develop...
In recent years, the bag of visual words (BoV) model is very popular in the field of computer vision. In particular, it has been widely used for image classification. The earliest method based on the BoV model, when constructing the visual dictionary, uses the clustering method to generate a single visual dictionary. This method does not consider the differences between categories of image dataset,...
This paper proposes a new Spatial Pyramid representation approach for image classification. Unlike the conventional Spatial Pyramid, the proposed method is invariant to rotation changes in the images. This method works by partitioning an image into concentric rectangles and organizing them into a pyramid. Each pyramidal region is then represented using a histogram of visual words. Our experimental...
Classification of high-resolution remote-sensing images is a challenging research area. In this paper we proposed a novel decision fusion framework to combine bag of features (BOF) based classifiers. The proposed framework, can also be used in multi category image classification applications. A single voting algorithm is used for decision fusion and an ambiguity detection module is used to determine...
The Spatial Pyramid Matching approach has become very popular to model images as sets of local bag-of-words. The image comparison is then done region-by-region with an intersection kernel. Despite its success, this model presents some limitations: the grid partitioning is predefined and identical for all images and the matching is sensitive to intra- and inter-class variations. In this paper, we propose...
Motivated by the widespread adoption of social networks and the abundant availability of user-generated multimedia content, our purpose in this work is to investigate how the known principles of active learning for image classification fit in this newly developed context. The process of active learning can be fully automated in this social context by replacing the human oracle with the user tagged...
To improve remote sensing image classification precision, we propose a novel method which is based on super pixel and adaptive weighted K-Means. First, super pixel segmentation algorithm is used to divide input images into irregular blocks which remain their semantic information and boundaries. And then, SIFT, GIST, Census, Gabor, and Color histogram, and many other types of features are extracted...
This paper develops a new algorithm based on Bag-of-Word to reflect spatial relationship of objects for visual object categorization. Beyond existing spatial pyramid for image representation, our contributions are the following: 1) we propose a combinational detector based on Maximally Stable Extremal Regions detector and Hessian-Laplacian detector to extract more discriminative features; 2) for object...
This article puts forward a kind of huge amounts of multi-object image recognition method -- BVCNN. Firstly, BING method is used to recognize images, which greatly reduces the time of estimating image targets, and makes it possible that quickly identify multiple target images, compared to traditional convolution neural networks only achieving single target image recognition, Secondly, vectorization...
This paper presents a novel road/terrain classification system based on the analysis of volunteered geographic information gathered by bikers. By ubiquitous collection of multi-sensor bike data, consisting of visual images, accelerometer information and GPS coordinates of the bikers' smartphone, the proposed system is able to distinguish between 6 different road/terrain types. In order to perform...
This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.
Multilabel image annotation is one of the most important open problems in computer vision field. Unlike existing works that usually use conventional visual features to annotate images, features based on deep learning have shown potential to achieve outstanding performance. In this work, we propose a multimodal deep learning framework, which aims to optimally integrate multiple deep neural networks...
This paper presents a novel image classification method based on integration of EEG and visual features. In the proposed method, we obtain classification results by separately using EEG and visual features. Furthermore, we merge the above classification results based on a kernelized version of Supervised learning from multiple experts and obtain the final classification result. In order to generate...
In this paper we take a look at extensions of the Bag of Words model developed within the last few years. Namely the aggregation of vector residuals known as VLAD encodings and Fisher kernels and assess their performance for the classification task using multiple views. We also take a look at the triangular embedding strategy for classification in the compression domain. Our work focuses on using...
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