446 research outputs found

    Learning Object Categories From Internet Image Searches

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    In this paper, we describe a simple approach to learning models of visual object categories from images gathered from Internet image search engines. The images for a given keyword are typically highly variable, with a large fraction being unrelated to the query term, and thus pose a challenging environment from which to learn. By training our models directly from Internet images, we remove the need to laboriously compile training data sets, required by most other recognition approaches-this opens up the possibility of learning object category models “on-the-fly.” We describe two simple approaches, derived from the probabilistic latent semantic analysis (pLSA) technique for text document analysis, that can be used to automatically learn object models from these data. We show two applications of the learned model: first, to rerank the images returned by the search engine, thus improving the quality of the search engine; and second, to recognize objects in other image data sets

    Character Type Classification via Probabilistic Topic Model

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    ArticleInternational Journal of Signal Processing, Image Processing and Pattern Recognition. 5(2): 123-140 (2012)journal articl

    A Review of Codebook Models in Patch-Based Visual Object Recognition

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    The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods

    Learning visual contexts for image annotation from Flickr groups

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    Learning visual contexts for image annotation from Flickr groups

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    Color Name Applications in Computer Vision

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    In Computer Vision, the association of names to colors is one of the fundamental problems in the field of image understanding. There are numerous computational applications (e.g. image retrieval, visual tracking, person identification, human-machine interaction, etc.) that require pixels to be labelled according to the color perceived by the user. This is relatively easy for focal colors under canonical illuminants, where the agreement is high, but becomes increasingly difficult as perceptions move away from these conditions. For these difficult cases, the traditional solution tends to be a collection of "ad-hoc" strategies, however, new approaches that combine knowledge from anthropology, linguistics, visual perception and machine learning have offered promising results. Specifically, deep neural networks appear to possess all the required building blocks to offer a color naming solution "in the wild". This article reviews the current state of knowledge and discusses open challenges with a multidisciplinary (and non-specialized) readership in mind
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