One of the prevalent learning tasks involving images is content-based image classification. This is a difficult task especially because the low-level features used to digitally describe images usually capture little information about the semantics of the images. In this paper, we tackle this difficulty by enriching the semantic content of the image representation by using external knowledge. The underlying hypothesis of our work is that creating a more semantically rich representation for images would yield higher machine learning performances, without the need to modify the learning algorithms themselves. The external semantic information is presented under the form of non-positional image labels, therefore positioning our work in a weakly supervised context. Two approaches are proposed: the first one leverages the labels into the visual vocabulary construction algorithm, the result being dedicated visual vocabularies. The second approach adds a filtering phase as a pre-processing of the vocabulary construction. Known positive and known negative sets are constructed and features that are unlikely to be associated with the objects denoted by the labels are filtered. We apply our proposition to the task of content-based image classification and we show that semantically enriching the image representation yields higher classification performances than the baseline representation.
Over the past decades computer vision domain has seen a large interest from the research community. Its application are larger than image analysis and include augmented reality, robotic vision, gesture recognition Whatsoever, in the context of Internet-originating images, one of the prevailing task is content-based image classification. Some of the initial image classification systems used color histograms @cite_15 for image representation. Such a representation does not retain any information about the shapes of objects in images and obtains moderate results. Other systems @cite_7 @cite_24 @cite_44 @cite_34 rely on texture detection. Texture is characterized by the repetition of basic elements or . For stochastic textures, it is the identity of the textons, not their spatial arrangement, that matters. The orderless representation has imposed itself as the state-of-the-art in image representation, for classification and indexation purposes. The process of constructing the representation includes sampling the image ( in Figure ), describing each features using an appearance-based descriptor (), constructing a visual vocabulary () and describing images as histograms over the visual words ().
@cite_7 @cite_24 @cite_44 @cite_15 @cite_34
2129739837 2119067550 1497592982 2129976136
A procedure is developed to extract numerical features which characterize the pore structure of reservoir rocks. The procedure is based on a set of descriptors which give a statistical description of porous media. These features are evaluated from digitized photomicrographs of reservoir rocks and they characterize the rock grain structure in term of (1) the linear dependency of grey tones in the photomicrograph image, (2) the degree of "homogeneity" of the image and (3) the angular variations of the image grey tone dependencies. On the basis of these textural features, a simple identification rule using piecewise linear discriminant functions is developed for categorizing the photomicrograph images. The procedure was applied to a set of 243 distinct images comprising 6 distinct rock categories. The coefficients of the discriminant functions were obtained using 143 training samples. The remaining (100) samples were then processed, each sample being assigned to one of 6 possible sandstone categories. Eighty-nine per cent of the test samples were correctly identified. This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine-invariant local patches is extracted from the image. This spatial selection process permits the computation of characteristic scale and neighborhood shape for every texture element. The proposed texture representation is evaluated in retrieval and classification tasks using the entire Brodatz database and a collection of photographs of textured surfaces taken from different viewpoints. A simple method for texture-based segmentation of colored images combining the Hotelling transform and an unsupervised neural network is presented. The method separates the image into n regions representing n possible existing textures. The Hotelling transform is used to improve the color contrast and achieve uncorrelated color components. Auto-contrast is then applied to get a better color spreading. Finally, a set of features extracted from the transformed image is submitted to a competitive neural network for training. Once finished, the neural network parameters are used to label the image regions according to their textures. We question the role that large scale filter banks have traditionally played in texture classification. It is demonstrated that textures can be classified using the joint distribution of intensity values over extremely compact neighborhoods (starting from as small as 3 spl times 3 pixels square), and that this outperforms classification using filter banks with large support. We develop a novel texton based representation, which is suited to modeling this joint neighborhood distribution for MRFs. The representation is learnt from training images, and then used to classify novel images (with unknown viewpoint and lighting) into texture classes. The power of the method is demonstrated by classifying over 2800 images of all 61 textures present in the Columbia-Utrecht database. The classification performance surpasses that of recent state-of-the-art filter bank based classifiers such as Leung & Malik, Cula & Dana, and Varma & Zisserman.