text: In this paper we present an extensive evaluation of visual descriptors for the content-based retrieval of remote sensing (RS) images. The evaluation includes global hand-crafted, local hand-crafted, and Convolutional Neural Network (CNNs) features coupled with four different Content-Based Image Retrieval schemes. We conducted all the experiments on two publicly available datasets: the 21-class UC Merced Land Use Land Cover (LandUse) dataset and 19-class High-resolution Satellite Scene dataset (SceneSat). The content of RS images might be quite heterogeneous, ranging from images containing fine grained textures, to coarse grained ones or to images containing objects. It is therefore not obvious in this domain, which descriptor should be employed to describe images having such a variability. Results demonstrate that CNN-based features perform better than both global and and local hand-crafted features whatever is the retrieval scheme adopted. Features extracted from SatResNet-50, a residual CNN suitable fine-tuned on the RS domain, shows much better performance than a residual CNN pre-trained on multimedia scene and object images. Features extracted from NetVLAD, a CNN that considers both CNN and local features, works better than others CNN solutions on those images that contain fine-grained textures and objects.
  @cite_36: In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives.
  @cite_44: The rapidly increasing volume of visual Earth Observation data calls for effective content based image retrieval solutions, specifically tailored for their high spatial resolution and heterogeneous content. In this paper, we address this issue with a novel local implementation of the well-known morphological descriptors called pattern spectra. They are computationally efficient histogram-like structures describing the global distribution of arbitrarily defined attributes of connected image components. Besides employing pattern spectra for the first time in this context, our main contribution lies in their dense calculation, at a local scale, thus enabling their combination with sophisticated visual vocabulary strategies. The Merced Landuse Landcover dataset has been used for comparing the proposed strategy against alternative global and local content description methods, where the introduced approach is shown to yield promising performances.
  @cite_18: The recognition of color texture under varying lighting conditions remains an open issue. Several features have been proposed for this purpose, ranging from traditional statistical descriptors to features extracted with neural networks. Still, it is not completely clear under what circumstances a feature performs better than others. In this paper, we report an extensive comparison of old and new texture features, with and without a color normalization step, with a particular focus on how these features are affected by small and large variations in the lighting conditions. The evaluation is performed on a new texture database, which includes 68 samples of raw food acquired under 46 conditions that present single and combined variations of light color, direction, and intensity. The database allows us to systematically investigate the robustness of texture descriptors across large variations of imaging conditions.
  @cite_60: This paper deals with a new low level feature that is extracted from the images and can be used for indexing and retrieval. This feature is called "Color and Edge Directivity Descriptor" and incorporates color and texture information in a histogram. CEDD size is limited to 54 bytes per image, rendering this descriptor suitable for use in large image databases. One of the most important attribute of the CEDD is the low computational power needed for its extraction, in comparison with the needs of the most MPEG-7 descriptors. The objective measure called ANMRR is used to evaluate the performance of the proposed feature. An online demo that implements the proposed feature in an image retrieval system is available at: http: orpheus.ee.duth.gr image_retrieval.
  @cite_39: Preface. 1. Image Content Analysis and Description X. Zabulis, S.C. Orphanoudakis. 2. Local Features for Image Retrieval L. Van Gool, et al 3. Fast Invariant Feature Extraction for Image Retrieval S. Siggelkow, H. Burkhardt. 4. Shape Description and Search for Similar Objects in Image Databases L.J. Latecki, R. Lakaemper. 5. Features in Content-based Image Retrieval Systems: a Survey R.C. Veltkamp, et al 6. Probablistic Image Models for Object Recognition and Pose Estimation J. Hornegger, H. Niemann. 7. Distribution-based Image Similarity J. Puzicha. 8. Distribution Free Statistics for Segmentation G. Frederix, E.J. Pauwels. 9. Information Retrieval Methods for Multimedia Objects N. Fuhr. 10. New descriptors for image and video indexing P. Gros, et al 11. Facial and Motion Analysis for Image and Video Retrieval M. Tistarelli, E. Grosso. 12. Asymmetric Similarity Measures for Video Summarisation S.M. Iacob, et al 13. Video Retrieval using Semantic Data A. Del Bimbo. 14. Adaptable Similarity Search in Large Image Databases T. Seidl, H.-P. Kriegel. 15. Parallel NN-search for large multimedia repositories R. Weber, et al
  @cite_23: Texture analysis is one of the fundamental aspects of human vision by which we discriminate between surfaces and objects. In a similar manner, computer vision can take advantage of the cues provided by surface texture to distinguish and recognize objects. In computer vision, texture analysis may be used alone or in combination with other sensed features (e.g. color, shape, or motion) to perform the task of recognition. Either way, it is a feature of paramount importance and boasts a tremendous body of work in terms of both research and applications.Currently, the main approaches to texture analysis must be sought out through a variety of research papers. This collection of chapters brings together in one handy volume the major topics of importance, and categorizes the various techniques into comprehensible concepts. The methods covered will not only be relevant to those working in computer vision, but will also be of benefit to the computer graphics, psychophysics, and pattern recognition communities, academic or industrial.
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  @cite_50: 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.
  @cite_34: Image content based retrieval is emerging as an important research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular using texture information for browsing and retrieval of large image data. We propose the use of Gabor wavelet features for texture analysis and provide a comprehensive experimental evaluation. Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy. An application to browsing large air photos is illustrated.
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  @cite_25: In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space in which scenes sharing membership in semantic categories (e.g., streets, highways, coasts) are projected closed together. The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
  @cite_12: 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.).
  @cite_61: Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.
  @cite_33: 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. >
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  @cite_45: An experimental comparison of a large number of different image descriptors for content-based image retrieval is presented. Many of the papers describing new techniques and descriptors for content-based image retrieval describe their newly proposed methods as most appropriate without giving an in-depth comparison with all methods that were proposed earlier. In this paper, we first give an overview of a large variety of features for content-based image retrieval and compare them quantitatively on four different tasks: stock photo retrieval, personal photo collection retrieval, building retrieval, and medical image retrieval. For the experiments, five different, publicly available image databases are used and the retrieval performance of the features is analyzed in detail. This allows for a direct comparison of all features considered in this work and furthermore will allow a comparison of newly proposed features to these in the future. Additionally, the correlation of the features is analyzed, which opens the way for a simple and intuitive method to find an initial set of suitable features for a new task. The article concludes with recommendations which features perform well for what type of data. Interestingly, the often used, but very simple, color histogram performs well in the comparison and thus can be recommended as a simple baseline for many applications.
  @cite_59: 
summary: Global hand-crafted features describe an image as a whole in terms of color, texture and shape distributions @cite_23 . Some notable examples of global features are color histograms @cite_33 , spatial histogram @cite_12 , Gabor filters @cite_34 , co-occurrence matrices @cite_76 @cite_36 , Local Binary Patterns (LBP) @cite_61 , Color and Edge Directivity Descriptor (CEDD) @cite_60 , Histogram of Oriented Gradients (HOG) @cite_26 , morphological operators like granulometries information @cite_44 @cite_59 @cite_56 , Dual Tree Complex Wavelet Transform (DT-CWT) @cite_18 @cite_1 and GIST @cite_25 . Readers who would wish to deepen the subject can refer to the following papers @cite_24 @cite_45 @cite_50 @cite_39 .