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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...
Bag-of-Features (BOF) representation is a very popular model for content based image classification. In BOF, term frequency (tf) and inverse document frequency (idf) is a very popular model to compute the weights of the visual vocabularies. However, tf-idf model does not contain the class information of images. Fortunately, chi-square model contains the class information well. So, in order to enhance...
Bag-of-features (BoFs) representation has been extensively applied to deal with various computer vision applications. To extract discriminative and descriptive BoF, one important step is to learn a good dictionary to minimize the quantization loss between local features and codewords. While most existing visual dictionary learning approaches are engaged with unsupervised feature quantization, the...
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...
In recent years, a content-based method such as `bag-of-features' (BoF) is coming to the fore as an object recognition and classification technique. This paper proposes a BoF signature using invariant region descriptor for object retrieval. The region descriptors are extracted from dense sampled regions in the training images. These descriptors are quantized by hierarchical k-means clustering in a...
We propose a bag-of-hierarchical-co-occurrence features method incorporating hierarchical structures for image classification. Local co-occurrences of visual words effectively characterize the spatial alignment of objects' components. The visual words are hierarchically constructed in the feature space, which helps us to extract higher-level words and to avoid quantization error in assigning the words...
The contributions of image blocks to the holistic scene semantic classification are further exploited in this paper. An image is subdivided into non-overlapping regular grid of blocks hierarchically, 2 × 2 blocks at the first level and 3 × 3 blocks at the second level. For each level, “bag-of-features” strategy is deployed to predict the scene category of each block. Then the holistic scene category...
Context plays a valuable role in any image understanding task confirmed by numerous studies which have shown the importance of contextual information in computer vision tasks, like object detection, scene classification and image retrieval. Studies of human perception on the tasks of scene classification and visual search have shown that human visual system makes extensive use of contextual information...
Image category recognition is important to access visual information on the level of objects and scene types. This paper combines different feature representations of images and learn a compact subspace of different features for the automatic recognition of object and scene classes. Compact visual-words and low-level-features object class subspaces are automatically learned from a set of training...
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