The typical content-based image retrieval problem is to find images within a database that are similar to a given query image. This paper presents a solution to a different problem, namely that of content based sub-image retrieval, i.e., finding images from a database that contains another image. Note that this is different from finding a region in a (segmented) image that is similar to another image region given as a query. We present a technique for CBsIR that explores relevance feedback, i.e., the user's input on intermediary results, in order to improve retrieval efficiency. Upon modeling images as a set of overlapping and recursive tiles, we use a tile re-weighting scheme that assigns penalties to each tile of the database images and updates the tile penalties for all relevant images retrieved at each iteration using both the relevant and irrelevant images identified by the user. Each tile is modeled by means of its color content using a compact but very efficient method which can, indirectly, capture some notion of texture as well, despite the fact that only color information is maintained. Performance evaluation on a largely heterogeneous dataset of over 10,000 images shows that the system can achieve a stable average recall value of 70 within the top 20 retrieved (and presented) images after only 5 iterations, with each such iteration taking about 2 seconds on an off-the-shelf desktop computer.
An straigthforward way to model images is to use its average color. This is obviously not effective in any non-trivial situation. Another simple, and in many situations cost-effective means is to use a global color histogram (GCH) (c.f., @cite_14 ). A common critique to GCHs is that it is unable to capture any notion of spatial distribution. To address this several other approaches have been proposed A comprehensive survey thereof is beyond the scope of this paper. , but they add complexity as a trade-off in order to gain effectiveness. Nevertheless, the use of color only, without any notion of spatial distribution, may be effective, if one is able to capture other features of the images, e.g., texture. That is exactly the advantage of the BIC technique proposed in @cite_0 and which we re-use within our proposal.
@cite_0 @cite_14
2039388884 2108486992
Demosaicing is a process of obtaining a full-color image by interpolating the missing colors of an image captured from a single sensor color filter array. This paper provides an effective and low-complexity iterative demosaicing algorithm applying a weighted-edge interpolation to handle green pixels followed by a series of color-difference interpolation to update red, blue, and green pixels. Based on our experiments of images, we enable the algorithm a well-designed stopping condition and pre-determine the proper weights of interpolation. Experimental results show that the proposed method performs much better than three state-of-the-art demosaicing techniques in terms of both computational cost and image quality. In comparison to the algorithm of successive approximation, the algorithm proposed here reduces mean squared error up to 14.5 while requiring computational cost only 22 on average. That is, it takes less time but performs better. Color histograms lack spatial information and are sensitive to intensity variation, color distortion and cropping. As a result, images with similar histograms may have totally different semantics. The region-based approaches are introduced to overcome the above limitations, but due to the inaccurate segmentation, these systems may partition an object into several regions that may have confused users in selecting the proper regions. In this paper, we present a robust image retrieval based on color histogram of local feature regions (LFR). Firstly, the steady image feature points are extracted by using multi-scale Harris-Laplace detector. Then, the significant local feature regions are ascertained adaptively according to the feature scale theory. Finally, the color histogram of local feature regions is constructed, and the similarity between color images is computed by using the color histogram of LFRs. Experimental results show that the proposed color image retrieval is more accurate and efficient in retrieving the user-interested images. Especially, it is robust to some classic transformations (additive noise, affine transformation including translation, rotation and scale effects, partial visibility, etc.). We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogram robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing retrieval. Color-based trackers recently proposed in [3,4,5] have been proved robust and versatile for a modest computational cost. They are especially appealing for tracking tasks where the spatial structure of the tracked objects exhibits such a dramatic variability that trackers based on a space-dependent appearance reference would break down very fast. Trackers in [3,4,5] rely on the deterministic search of a window whose color content matches a reference histogram color model.Relying on the same principle of color histogram distance, but within a probabilistic framework, we introduce a new Monte Carlo tracking technique. The use of a particle filter allows us to better handle color clutter in the background, as well as complete occlusion of the tracked entities over a few frames.This probabilistic approach is very flexible and can be extended in a number of useful ways. In particular, we introduce the following ingredients: multi-part color modeling to capture a rough spatial layout ignored by global histograms, incorporation of a background color model when relevant, and extension to multiple objects. Abstract Color histogram is the most commonly used color feature in image retrieval systems. However, this feature cannot effectively characterize an image, since it only captures the global properties. To make the retrieval more accurate, this paper introduces a run-length (RL) feature. The feature integrates the information of color and shape of the objects in an image. It can effectively discriminate the directions, areas and geometrical shapes of the objects. Yet, extracting the RL feature is time-consuming. For that reason, this paper also provides one revised representation of the RL, called semi-run-length (SRL). Based on the SRL feature, this paper develops an image retrieval system, and the experimental results show that the system gives an impressive performance. We propose a new technique for visual attribute transfer across images that may have very different appearance but have perceptually similar semantic structure. By visual attribute transfer, we mean transfer of visual information (such as color, tone, texture, and style) from one image to another. For example, one image could be that of a painting or a sketch while the other is a photo of a real scene, and both depict the same type of scene. Our technique finds semantically-meaningful dense correspondences between two input images. To accomplish this, it adapts the notion of "image analogy" with features extracted from a Deep Convolutional Neutral Network for matching; we call our technique Deep Image Analogy. A coarse-to-fine strategy is used to compute the nearest-neighbor field for generating the results. We validate the effectiveness of our proposed method in a variety of cases, including style texture transfer, color style swap, sketch painting to photo, and time lapse. We propose a new technique for visual attribute transfer across images that may have very different appearance but have perceptually similar semantic structure. By visual attribute transfer, we mean transfer of visual information (such as color, tone, texture, and style) from one image to another. For example, one image could be that of a painting or a sketch while the other is a photo of a real scene, and both depict the same type of scene. Our technique finds semantically-meaningful dense correspondences between two input images. To accomplish this, it adapts the notion of "image analogy" [ 2001] with features extracted from a Deep Convolutional Neutral Network for matching; we call our technique deep image analogy. A coarse-to-fine strategy is used to compute the nearest-neighbor field for generating the results. We validate the effectiveness of our proposed method in a variety of cases, including style texture transfer, color style swap, sketch painting to photo, and time lapse. The existence of large image datasets such as photos on the World Wide Web make it possible to build powerful generic models for low-level image attributes like color using simple histogram learning techniques. We describe the construction of color models for skin and non-skin classes from a dataset of nearly 1 billion labeled pixels. These classes exhibit a surprising degree of separability which we exploit by building a skin pixel detector that achieves an equal error rate of 88 . We compare the performance of histogram and mixture models in skin detection and find histogram models to be superior in accuracy and computational cost. Using aggregate features computed from the skin detector we build a remarkably effective detector for naked people. We believe this work is the most comprehensive and detailed exploration of skin color models to date. In this paper, we study the salient object detection problem for images. We formulate this problem as a binary labeling task where we separate the salient object from the background. We propose a set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. Further, we extend the proposed approach to detect a salient object from sequential images by introducing the dynamic salient features. We collected a large image database containing tens of thousands of carefully labeled images by multiple users and a video segment database, and conducted a set of experiments over them to demonstrate the effectiveness of the proposed approach. This paper presents a novel image feature representation method, namely color difference histograms (CDH), for image retrieval. This method is entirely different from the existing histograms; most of the existing histogram techniques merely count the number or frequency of pixels. However, the unique characteristic of CDHs is that they count the perceptually uniform color difference between two points under different backgrounds with regard to colors and edge orientations in L*a*b* color space. This method pays more attention to color, edge orientation and perceptually uniform color differences, and encodes color, orientation and perceptually uniform color difference via feature representation in a similar manner to the human visual system. The method can be considered as a novel visual attribute descriptor combining edge orientation, color and perceptually uniform color difference, as well as taking the spatial layout into account without any image segmentation, learning processes or clustering implementation. Experimental results demonstrate that it is much more efficient than the existing image feature descriptors that were originally developed for content-based image retrieval, such as MPEG-7 edge histogram descriptors, color autocorrelograms and multi-texton histograms. It has a strong discriminative power using the color, texture and shape features while accounting for spatial layout. In many multi-media applications it is desirable to separate the influence of the illumination sources and imaging equipment from the properties of the depicted scene. The ability of the human visual system to solve this task in many situations is known as color constancy. Technical applications of these methods include automatic color correction and illumination independent search in image databases. Many conventional computational color constancy methods assume that the effect of an illumination change can be described by a matrix multiplication with a diagonal matrix. In this paper we introduce a color normalization algorithm which computes the unique color transformation matrix which normalizes a given set of moments computed from the color distribution of an image. This normalization procedure is a generalization of the channel independent color constancy methods since general matrix transformations are considered. We compare the performance of this new normalization method with conventional color constancy methods. The experiments show that diagonal transformation matrices provide a better illumination compensation. This shows that the color moments also contain significant information about the color distributions of the objects in the image which is independent of the illumination characteristics. In another set of experiments we use the unique transformation matrix as a descriptor of the set of moments which describe the global color distribution in the image. Combining the matrices computed from two such images describes the color differences between them. We then use this as a tool for color dependent search in image databases. This matrix based color search is computationally less demanding than histogram based color search tools. This paper presents a novel unsupervised method to transfer the style of an example image to a source image. The complex notion of image style is here considered as a local texture transfer, eventually coupled with a global color transfer. For the local texture transfer, we propose a new method based on an adaptive patch partition that captures the style of the example image and preserves the structure of the source image. More precisely, this example-based partition predicts how well a source patch matches an example patch. Results on various images show that our method outperforms the most recent techniques. This paper proposes a color feature description for image and video retrieving applications such as personal video recorder, which manages a large amount of data. This descriptor specifies the spatial distribution of colors with a few nonlinear quantized DCT coefficients of grid based average colors. The tradeoff between the number of the coefficients enclosed in the descriptor and retrieval cost is studied. The experimental results show that the descriptor enclosing six for luminance and three for each chrominance coefficient achieves the best trade-off between the storage cost and retrieval efficiency. It requires 6 bits for DC and 5 bits for AC coefficients, and therefore total storage cost is just 63 bits per image. This description, named color layout descriptor, has been accepted as a part of the MPEG-7 final committee draft. The skin probability maps are refined using discriminative features (DSPFs).Spatial analysis of skin pixels is more successful when performed in DSPF skin maps.The textural features are extracted from skin maps rather than from the grayscale. In this paper we propose a new method for skin detection in color images which consists in spatial analysis using the introduced texture-based discriminative skin-presence features. Color-based skin detection has been widely explored and many skin color modeling techniques were developed so far. However, efficacy of the pixel-wise classification is limited due to an overlap between the skin and non-skin pixels reported in many color spaces. To increase the discriminating power of the skin classification schemes, textural and spatial features are often exploited for skin modeling. Our contribution lies in using the proposed discriminative feature space as a domain for spatial analysis of skin pixels. Contrary to existing approaches, we extract the textural features from the skin probability maps rather than from the luminance channel. Presented experimental study confirms that the proposed method outperforms alternative skin detection techniques, which also involve analysis of textural and spatial features. In image retrieval based on color, the weighted distance between color histograms of two images, represented as a quadratic form, may be defined as a match measure. However, this distance measure is computationally expensive and it operates on high dimensional features (O(N)). We propose the use of low-dimensional, simple to compute distance measures between the color distributions, and show that these are lower bounds on the histogram distance measure. Results on color histogram matching in large image databases show that prefiltering with the simpler distance measures leads to significantly less time complexity because the quadratic histogram distance is now computed on a smaller set of images. The low-dimensional distance measure can also be used for indexing into the database. > This paper proposes a new definition of the averaging of discrete probability distributions as a barycenter over the Monge-Kantorovich optimal transport space. To overcome the time complexity involved by the numerical solving of such problem, the original Wasserstein metric is replaced by a sliced approximation over 1D distributions. This enables us to introduce a new fast gradient descent algorithm to compute Wasserstein barycenters of point clouds. This new notion of barycenter of probabilities is likely to find applications in computer vision where one wants to average features defined as distributions. We show an application to texture synthesis and mixing, where a texture is characterized by the distribution of the response to a multi-scale oriented filter bank. This leads to a simple way to navigate over a convex domain of color textures. Images captured under water are usually degraded due to the effects of absorption and scattering. Degraded underwater images show some limitations when they are used for display and analysis. For example, underwater images with low contrast and color cast decrease the accuracy rate of underwater object detection and marine biology recognition. To overcome those limitations, a systematic underwater image enhancement method, which includes an underwater image dehazing algorithm and a contrast enhancement algorithm, is proposed. Built on a minimum information loss principle, an effective underwater image dehazing algorithm is proposed to restore the visibility, color, and natural appearance of underwater images. A simple yet effective contrast enhancement algorithm is proposed based on a kind of histogram distribution prior, which increases the contrast and brightness of underwater images. The proposed method can yield two versions of enhanced output. One version with relatively genuine color and natural appearance is suitable for display. The other version with high contrast and brightness can be used for extracting more valuable information and unveiling more details. Simulation experiment, qualitative and quantitative comparisons, as well as color accuracy and application tests are conducted to evaluate the performance of the proposed method. Extensive experiments demonstrate that the proposed method achieves better visual quality, more valuable information, and more accurate color restoration than several state-of-the-art methods, even for underwater images taken under several challenging scenes. Local binary pattern (LBP) is widely adopted for efficient image feature description and simplicity. To describe the color images, it is required to combine the LBPs from each channel of the image. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. In order to cope with this problem, this paper proposes a novel method for image description with multichannel decoded LBPs. We introduce adder- and decoder-based two schemas for the combination of the LBPs from more than one channel. Image retrieval experiments are performed to observe the effectiveness of the proposed approaches and compared with the existing ways of multichannel techniques. The experiments are performed over 12 benchmark natural scene and color texture image databases, such as Corel-1k, MIT-VisTex, USPTex, Colored Brodatz, and so on. It is observed that the introduced multichannel adder- and decoder-based LBPs significantly improve the retrieval performance over each database and outperform the other multichannel-based approaches in terms of the average retrieval precision and average retrieval rate. We study visual attention by detecting a salient object in an input image. We formulate salient object detection as an image segmentation problem, where we separate the salient object from the image background. We propose a set of novel features including multi-scale contrast, center-surround histogram, and color spatial distribution to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. We also constructed a large image database containing tens of thousands of carefully labeled images by multiple users. To our knowledge, it is the first large image database for quantitative evaluation of visual attention algorithms. We validate our approach on this image database, which is public available with this paper. It is shown that the color histogram has an even closer relationship to scene properties than has been previously described. Color histograms have identifiable features that relate in a precise mathematical way to scene properties. Object color and illumination color are the most obvious properties that are related to color distribution, and their extraction has already been described. It is shown here that the histogram of color variation may be further exploited to relate its shape to surface roughness and imaging geometry. An understanding of these features allows an improved estimate of illumination color and object color to be made. >