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We describe an image representation that combines the representational power of graphs with the efficiency of the bag-of-words model. For each image in a data set, first, a graph is constructed from local patches of interest regions and their spatial arrangements. Then, each graph is represented with a histogram of sub graphs selected using a frequent subgraph mining algorithm in the whole data. Using...
The work in this paper deals with the learning of gradual rules in the framework of data classification. Gradual rules are well suited to express constraints between numerical quantities. They are here used to constrain the shape of classes to be modeled. More precisely, it is proposed to represent convex polygon-shaped classes by means of "If-Then" classification gradual rules. The latter,...
This paper designs a novel hiding strategy based on an equivalence relation, which can remarkably enhance the quality of stego image without sacrificing the security and capacity of original steganography schemes. According to a constructed equivalence relation based on the capacity of hiding units, all hiding units can be partitioned into equivalence classes. Following that, the hiding procedure...
In this paper, an efficient edge-directed demosaicing algorithm for Bayer images is presented. A new edge-sensing measure called integrated gradient (IG) is exploited to extract gradient information in both color intensity and color difference domains simultaneously for the interpolations. This measure supports full resolution and allows one to interpolate the missing samples along an appropriate...
Moving objects detection in dynamic scenes is a challenging task in many computer vision applications. Traditional background modeling methods do not work well in these situations since they assume a nearly static background. In this paper, a novel operator named spatial extended center-symmetric local binary pattern (SCS-LBP) for background modeling is proposed. It extracts spatial and temporal information...
We propose a method to detect events and event boundaries in soccer videos by using web-casting texts and audio-visual features. The events and their inaccurate time information given in web-casting texts need to be aligned with the visual content of the video. We overcome this issue by utilizing textual, visual and audio features. Existing methods assume that the time at which the event occurs is...
In this paper, we propose a reversible data hiding method with low time complexity and high embedding capacity for gray-scale images. This method presents a block-based lossless data hiding schema to utilize the similarity between neighborhood pixels in the block to improve the marked-image quality. The experimental results show that our method has increased the hiding capacity with keeping acceptable...
This paper presents a QEM-based simplification approach for extracting and delineating building footprints from Airborne LiDAR data. Our approach consists of three steps: first of all, Digital Surface Model (DSM) is generated from the raw point cloud by using the interpolation method. Secondly, the potential points on building outlines have to be aggregated to form connected building blobs. Those...
Information mining from heavy SAR images is considered from the point of view of the procedure automatization. Two schemes based on Neural Networks are evaluated, one based on the Self Organizing Map method exploiting polarimetric information and oriented to land cover classification, the other based on the Pulse-Coupled Neural Networks aiming at characterizing the imaged buildings.
This paper discusses methods and parameter settings that help to estimate texture in SAR images. In general, this is a difficult task for SAR images that are characterized by speckle noise and which span a wide range of pixel magnitudes. We applied Gauss Markov Random Field (GMRF) models and Enhanced Model Based Despeckling (EMBD) to 1 meter resolution amplitude images of the German TerraSAR-X mission...
This paper discusses the basic paradigm of how image information mining methods work in the field of remote sensing. To this end, we compare our approaches to the approaches being used in the world of multimedia; then we discuss the annotation specifics of remote sensing data and describe the different types of remote sensing data that we are faced with today. We conclude with a description of algorithmic...
This paper addresses the problem of change detection (CD) in very high geometrical resolution (VHR) multitemporal images. In this context, we propose a general conceptual framework that aims at giving: i) a taxonomy of different radiometric changes occurring when dealing with VHR remote sensing images; and ii) a global approach to the definition of the architecture of effective CD methods for VHR...
In this paper, we investigate the use of random-projection-based dimensionality reduction for hyperspectral endmember extraction. It is data-independent and computationally more efficient than other widely used dimensionality reduction methods, such as principal component analysis and maximum noise fraction transform. Based on the preliminary result, random-projection-based dimensionality reduction...
This paper presents an original data mining approach for extracting pixel evolutions and sub-evolutions from Satellite Image Time Series. These patterns, called frequent grouped sequential patterns, represent the (sub-)evolutions of pixels over time, and have to satisfy two constraints: firstly to correspond to at least a given minimum surface and secondly to be shared by pixels that are sufficiently...
In this paper, we develop a new spatial preprocessing strategy which can be applied prior to a spectral-based endmember extraction process for unmixing of hyperspectral data. Our proposed approach directs the endmember searching process to regions which are both spectrally pure and spatially homogeneous in the scene. Our experimental results, conducted using simulated hyperspectral data sets with...
The IR-MAD components show changes in the agricultural areas as well as in the mine, and the kMAF components focus on extreme changes in the mine. Due to lack of change in the spectral signal (the change occurs in the height of the surface only) excavation of material (here brown coal) leaving the same material behind is not detected.
This paper introduces an algorithm for capturing high complexity regions of a data domain. In this work, we focus on domains in R2. In particular, we analyze 2-dimensional image domains. Two different methods for mining are considered. The first method performs an information-theoretic analysis based on entropy to find diverse areas. The second method applies the concept of box-counting dimension...
In this paper, we propose a hybrid data hiding method to embed three secret bits to four cover pixels. In the proposed method, a Hamming code word consists of three parity check bits whose values are identical to three secret bits and four data bits whose values are derived by performing XOR Function on four cover pixels. In order to keep the Hamming code word satisfying the condition of (7, 4) Hamming...
An approach to Region Of Interest Based Image Classification (ROIBIC), based on a time series analysis approach, is described. The focus of the approach is the classification of MRI brain scan data according to the nature of the corpus callosum (a feature within such scans), however the approach also has general applicability. The advocated approach combines a number of image processing techniques...
Considering the characteristic that the nucleus and the nucleolus have no obvious boundary, this paper presents a fast level set method which is based on the overall information of an image to extract the nucleolus from the prostate nucleus. We pre-process the three-dimensional colorful prostate image at first, making the nucleolus (target) distinct to the surrounding environment (background) at the...
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