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This paper presents a novel approach to visual objects classification based on generating simple fuzzy classifiers using local image features to distinguish between one known class and other classes. Boosting meta-learning is used to find the most representative local features. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation...
Through the linear correlation analysis between the local feature and its K-nearest-neighbor visual words and significance testing of locality-constrained linear coding, this paper finds that the fundamental reason for causing nonsignificance of the weight coefficient is the multicollinearity of K-nearest-neighbor visual words in Locality-constrained Linear Coding (LLC) scheme. Locality-constrained...
Since the seminal work of Thrun [16], the learning to learn paradigm has been defined as the ability of an agent to improve its performance at each task with experience, with the number of tasks. Within the object categorization domain, the visual learning community has actively declined this paradigm in the transfer learning setting. Almost all proposed methods focus on category detection problems,...
In recent years, the Bag-of-visual Words image representation has led to many significant results in visual object recognition and categorization. However, experiments show that the unsupervised clustering of primitive visual features tends to result in the limited discriminative ability of the visual codebook, since it does not take the spatial relationship between visual primitives into consideration...
Visual object categorization is one of the most active research topics in computer vision, and Caltech-101 data set is one of the standard benchmarks for evaluating the method performance. Despite of its wide use, the data set has certain weaknesses: (i) the objects are practically in a standard pose and scale in the middle of the images and (ii) background varies too little in certain categories...
Since the challenging visual object categorization has attracted more and more attention in recent years, we present in this paper a novel approach called statistical measures based image modeling for this problem, thus avoiding the major difficulty of the popular “bag-of-visual words” approach which needs to fix a visual vocabulary size. We use a series of statistical measures over our proper region...
Visual object categorization has gained more and more attention in computer vision and bag-of-features model has become an important approach to form an object categorization system. As for image feature representation, "continuous valued" histogram that records frequency of each visual word and "binarized value" histogram that records only absence/presence of each visual word...
In this paper, we consider the problem of classifying a real world image to the corresponding object class based on its visual content via sparse representation, which is originally used as a powerful tool for acquiring, representing and compressing high-dimensional signals. Assuming the intuitive hypothesis that an image could be represented by a linear combination of the training images from the...
We present promising results for visual object categorization, obtained with adaBoost using new original "keypoints-based features". These weak-classifiers produce a Boolean response based on presence or absence in the tested image of a "keypoint" (a kind of SURF interest point) with a descriptor sufficiently similar (i.e. within a given distance) to a reference descriptor characterizing...
We propose a new Web image gathering system which employs the region-based bag-of-features representation and multiple instance learning. The contribution of this work is introducing the region-based bag-of-features representation into an Web image gathering task where training data is incomplete and having proved its effectiveness by comparing the proposed method with the normal whole-image-based...
This paper presents a method for visual object categorization based on encoding the joint textural information in objects and the surrounding background, and requiring no segmentation during recognition. The framework can be used together with various learning techniques and model representations. Here we use this framework with simple probabilistic models and more complex representations obtained...
We present a higher-level visual representation, visual synset, for object categorization. The visual synset improves the traditional bag of words representation with better discrimination and invariance power. First, the approach strengthens the inter-class discrimination power by constructing an intermediate visual descriptor, delta visual phrase, from frequently co-occurring visual word-set with...
In the task of visual object categorization, semantic context can play the very important role of reducing ambiguity in objects' visual appearance. In this work we propose to incorporate semantic object context as a post-processing step into any off-the-shelf object categorization model. Using a conditional random field (CRF) framework, our approach maximizes object label agreement according to contextual...
The aim of this work is the evaluation of different multi-scale filter banks, mainly based on oriented Gaussian derivatives and Gabor functions, to be used in the generation of robust features for visual object categorization. In order to combine the responses obtained from several spatial scales, we use the biologically inspired HMAX model (Riesenhuber and Poggio, 1999). We have tested the different...
Studies of high-level models of visual object categorization have left unresolved issues of neurobiological relevance, including how features are extracted from the image and the role played by memory capacity in categorization performance. We compared the ability of a comprehensive set of models to match the categorization performance of human observers while explicitly accounting for the models'...
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