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Deep learning methods have been effectively used to provide great improvement in various research fields such as machine learning, image processing and computer vision. One of the most frequently used deep learning methods in image processing is the convolutional neural networks. Compared to the traditional artificial neural networks, convolutional neural networks do not use the predefined kernels,...
Although many region based models for image auto-annotation have been proposed recently, their performances are not satisfactory due to the sensitivity to segmentation errors. In this paper, by evaluating two image partition methods and four visual features, we propose a new ensemble method under Multi-Instance Multi-Label (MIML) learning framework which has been proposed recently. The ensemble method...
In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel...
We describe a system for generating coherent movies from a collection of unedited videos. The generation process is guided by one or more input keyframes, which determine the content of the generated video. The basic mechanism involves similarity analysis using the histogram intersection function. The function is applied to spatial pyramid histograms computed on the video frames in the collection...
In this paper, we have introduced a hierarchical object categorization method with automatic feature selection. A hierarchy obtained by natural similarities and properties is learnt by automatically selected features at different levels. The categorization is a top-down process yielding multiple labels for a test object. We have tested out method and compared the experimental results with that of...
Character recognition has been in importance for several decades. Lot of research interest are now focused on applying pattern recognition and computer vision algorithms on camera captured documents to retrieve information from the documents. This paper presents a novel approach for extracting text in camera captured images using edge based algorithm. Extensive experiments have been carried out on...
Measuring image similarity is a central topic in computer vision. In this paper, we learn similarity from Flickr groups and use it to organize photos. Two images are similar if they are likely to belong to the same Flickr groups. Our approach is enabled by a fast Stochastic Intersection Kernel MAchine (SIKMA) training algorithm, which we propose. This proposed training method will be useful for many...
Feature-based methods have recently gained popularity in computer vision and pattern recognition communities, in applications such as object recognition and image retrieval. In this paper, we explore analogous approaches in the 3D world applied to the problem of non-rigid shape search and retrieval in large databases.
Linear discriminant analysis (LDA) is a popular feature extraction method that has aroused considerable interests in computer vision and pattern recognition fields. The projection vectors of LDA is usually achieved by maximizing the between-class scatter and simultaneously minimizing the within-class scatter of the data set. However, in practice, there is usually a lack of sufficient labeled data,...
Automatic semantic scene classification is a challenging research topic in computer vision and it is also a promising solution to scene understanding and image semantic retrieval. In this paper, novel techniques are proposed to implement multi-semantic scene classification. We first extract some regions of interest (ROIs) from each image based on image-driven, bottom-up visual attention model, and...
A new feature descriptor is presented for object and scene recognition. The new approach, called CDIKP, uniquely combines the scale-invariant feature detection with a robust projection kernel technique to produce highly efficient feature representation. The produced feature descriptors are highly-compact in comparisons to the state-of-the-art, do not require any pretraining step, and show superior...
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