Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on content-aware synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategy since they have different natures. We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image targeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.
The adaptive integration of the color and texture attributes in image segmentation is one of the most investigated topics of research in computer vision (surveyed in @cite_39 ). However, most of the image segmentation algorithm do not clearly illustrate the type of each region (textural or non-textural) in the result. @cite_9 present a fast texture descriptor based on LU transform, but how to determine if a pixel is texture or non-texture according to the feature values is not discussed. @cite_5 present an intuitive texture detection method which is based on contrast and disorganization measurements of image blocks. The method is not effective on noisy images which tend to have decreasing contrast and often generate many disjoint areas. Todorovic and Ahuja @cite_38 formulate the detection of texture subimages as identifying modes of the pdf of region descriptors. However, the method is not efficient (5 minutes for a @math image) for practical image retargeting applications.
@cite_5 @cite_9 @cite_38 @cite_39
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Given an arbitrary image, our goal is to segment all distinct texture subimages. This is done by discovering distinct, cohesive groups of spatially repeating patterns, called texels, in the image, where each group defines the corresponding texture. Texels occupy image regions, whose photometric, geometric, structural, and spatial-layout properties are samples from an unknown pdf. If the image contains texture, by definition, the image will also contain a large number of statistically similar texels. This, in turn, will give rise to modes in the pdf of region properties. Texture segmentation can thus be formulated as identifying modes of this pdf. To this end, first, we use a low-level, multiscale segmentation to extract image regions at all scales present. Then, we use the meanshift with a new, variable-bandwidth, hierarchical kernel to identify modes of the pdf defined over the extracted hierarchy of image regions. The hierarchical kernel is aimed at capturing texel substructure. Experiments demonstrate that accounting for the structural properties of texels is critical for texture segmentation, leading to competitive performance vs. the state of the art. The adaptive integration of the colour and texture attributes in the development of complex image descriptors is one of the most investigated topics of research in computer vision. The substantial interest shown by the research community in colour-texture-based segmentation is mainly motivated by two factors. The first is related to the observation that the imaged objects are often described at perceptual level by distinctive colour and texture characteristics, while the second is motivated by the large spectrum of possible applications that can be addressed by the colour-texture integration in the segmentation process. Over the past three decades a substantial number of techniques in the field of colour-texture segmentation have been reported and it is the aim of this article to thoroughly evaluate and categorise the most relevant algorithms with respect to the modality behind the integration of these two fundamental image attributes. In this paper we also provide a detailed discussion about data collections, evaluation metrics and we review the performance attained by state of the art implementations. We conclude with a discussion that samples our views on the field of colour-texture image segmentation and this is complemented with an examination of the potential future directions of research. We propose new dense descriptors for texture segmentation. Given a region of arbitrary shape in an image, these descriptors are formed from shape-dependent scale spaces of oriented gradients. These scale spaces are defined by Poisson-like partial differential equations. A key property of our new descriptors is that they do not aggregate image data across the boundary of the region, in contrast to existing descriptors based on aggregation of oriented gradients. As an example, we show how the descriptor can be incorporated in a Mumford-Shah energy for texture segmentation. We test our method on several challenging datasets for texture segmentation and textured object tracking. Experiments indicate that our descriptors lead to more accurate segmentation than non-shape dependent descriptors and the state-of-the-art in texture segmentation. 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.