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Effective image classification becomes an important issue in content-based image retrieval since it can help to organize the massive amount of digital images and serve for many applications such as object identification, web people search, etc. In this paper, the image classification problem is considered as a Multiple-Instance Learning problem, and Multiple-Instance Decision-Based Neural Networks...
In this paper, the framework of MapReduce is explored for large-scale multimedia data mining. Firstly, a brief overview of MapReduce and Hadoop is presented to speed up large-scale multimedia data mining. Then, the high-level theory and low-level implementation for several key computer vision technologies involved in this work are introduced, such as 2D/3D interest point detection, clustering, bag...
Traditional image classification approaches focused on utilizing a host of target data to learn an efficient classification model. However, these methods were generally based on the target data without considering auxiliary data. If the knowledge from auxiliary data could be successfully transferred to the target data, the performance of the model would be improved. In recent years, transfer learning...
Support Vector Machines (SVMs) have been extensively used for visual object classification to bridge the semantic gap between the low level features and high level concepts. SVM treats each training input equally during the construction of its decision surface which results in poor learning machines if training data include outliers. In this paper, a novel fuzzy visual object classification approach...
A fusing image classification algorithm is presented, which uses Bag-Of-Features model (BOF) as images' initial semantic features, and subsequently employs Fisher linear discriminative analysis (FLDA) algorithm to get its distribution in a linear optimal subspace as images' final features. Lastly images are classified by K nearest neighbor algorithm. The experimental results indicate that the image...
This paper proposes a cascaded classifier framework for better image recognition. The proposed method is based on the confidence values given by the classifiers. By using our proposed topN-Exemplar SVM in the second stage and comparing the confidence values with those from the first stage, the classification results with less confidence are successfully updated. The validity of our algorithm has been...
In this paper, we explain the bag of words representation from a soft computing perspective. The traditional Bag of word representation describes an image as a bag of discrete visual code words. Where histogram of the number of occurrences of these code words is used for image classification tasks. The drawback of the approach is that every visual feature in an image is assigned to single codeword,...
Recently, the Bag-of-visual Words (BoW) based image representation has drawn much attention in image categorization and retrieval applications. It is known that the visual codebook construction and the related quantization methods play the important roles in BoW model. Traditionally, visual codebook is generated by clustering local features into groups, and the original feature is hard quantized to...
In this paper we exploit the unique challenges and at the same time new opportunities of the medical domain, by developing methods with automatic diagnosis capacity. Our methods use the capabilities of the statistical framework for mathematical processing of images and image mining techniques for producing diagnosis rules. The experiments were carried out on a medical database with digestive images.
In the conventional bag of visual words (BoW) based image representation, single visual word is not discriminative enough and the spatial contextual information among local image features is ignored. In this paper, descriptive local feature groups are proposed to address these two problems. First, local image features are refined by slightly transforming the original image. Then they are clustered...
Spatial Pyramid Match lies at a heart of modern object category recognition systems. Once image descriptors are expressed as histograms of visual words, they are further deployed across spatial pyramid with coarse-to-fine spatial location grids. However, such representation results in extreme histogram vectors of 200K or more elements increasing computational and memory requirements. This paper investigates...
The local feature (e.g. SIFT) and Bag of Words (BOW) model play key roles in achieving a state-of-the-art performance for image classification. Although we realize that utilizing extra color information will undoubtedly boost the local feature, there still have not been any research that have carefully focused on how to efficiently transfer this color boosted local feature into a boosted BOW. In this...
This paper presents an improvement on a biologically inspired network for image classification. Previous models have used a multi-scale and multi-orientation architecture to gain robustness to transformations and to extract complex visual features. Our contribution to this type of architecture resides in the building of complex visual features which are better tuned to images structures. We allow...
In image classification, the most powerful statistical learning approaches are based on the Bag-of-Words paradigm. In this article, we propose an extension of this formalism. Considering the Bag-of-Features, dictionary coding and pooling steps, we propose to focus on the pooling step. Instead of using the classical sum or max pooling strategies, we introduced a density function-based pooling strategy...
Visual Word Uncertainty also referred to as Soft Assignment is a well established technique for representing images as histograms by flexible assignment of image descriptors to a visual vocabulary. Recently, an attention of the community dealing with the object category recognition has been drawn to Linear Coordinate Coding methods. In this work, we focus on Soft Assignment as it yields good results...
Within the Content Based Image Retrieval (CBIR) framework, three main points can be highlighted: visual descriptors extraction, image signatures and their associated similarity measures, and machine learning based relevance functions. While the first and the last points have vastly improved in recent years, this paper addresses the second point. We propose a novel approach to compute vector representations...
We tackle the challenge of web image classification using additional tags information. Unlike traditional methods that only use the combination of several low-level features, we try to use semantic concepts to represent images and corresponding tags. At first, we extract the latent topic information by probabilistic latent semantic analysis (pLSA) algorithm, and then use multi-label multiple kernel...
In this paper, we present a new method to improve the performance of current bag-of-word based image classification process. After feature extraction, we introduce a pair wise image matching scheme to select the discriminative features. Only the label information from the raining-sets is used to update the feature weights via an iterative matching processing. The selected features correspond to the...
The pornographic images recognition can be seen as a special kind of object recognition task,but current pornographic images filtering algorithms using BoVF approaches have some problems,such as the high false positive rate toward the bikinis images and insufficiency of filtering pornographic images with pornographic actions. The paper proposes a novel pornographic image filtering model using High-level...
Through the study of attention selection mechanism based on Amplitude Modulation Fourier Transform, a novel scene classification method based on maximum entropy policy and visual attention is proposed in this paper. This method adopts Amplitude Modulation Fourier Transform to construct saliency map, and adaptive Gaussian filter is used on the old saliency map to get the new information-rich saliency...
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