Effective location recommendation is an important problem in both research and industry. Much research has focused on personalized recommendation for users. However, there are more uses such as site selection for firms and factories. In this study, we try to solve site selection problem by recommending some locations satisfying special requirements. There are many factors affecting it, including functions of architecture, building cost, pollution discharge etc. We focus on the specific site selection of meteorological observation stations in this paper with leveraging the factors of functions of architecture and building cost from multi-source urban big data. We consider not only recommending the locations that can provide more accurate prediction and cover more areas, but also minimizing the cost of building new stations. We design an extensible two-stage framework for the station placing including prediction model and recommendation model. It is very convenient for executives to add more real-life factors into our approach. We have some empirical findings and evaluate the proposed approach using the real meteorological data of Shaanxi province, China. Experiment results show the better performance of our approach than existing commonly used methods.

Locations of images have been widely used in many application scenarios for large geotagged image corpora. As to images that are not geographically tagged, we estimate their locations with the help of the large geotagged image set by content-based image retrieval. Bag-of-words image representation has been utilized widely. However, the individual visual word-based image retrieval approach is not effective in expressing the salient relationships of image region. In this paper, we present an image location estimation approach by multisaliency enhancement. We first extract region-of-interests (ROIs) by mean-shift clustering on the visual words and salient map of the image based on which we further determine the importance of the ROI. Then, we describe each ROI by the spatial descriptors of visual words. Finally, region-based visual phrases are generated to further enhance the saliency in image location estimation. Experiments show the effectiveness of our proposed approach.

In H.264/AVC, the encoders have to try all the block types (16×16, 16×8, 8×16, 8×8, 8×4, 4×8 and 4×4) for each macro-block motion estimation and inter mode decision. All motion vectors and residuals are passed into Lagrange rate distortion (RD) cost model to calculate all RD cost values exhaustively, which introduce considerable computation complexity and resource demanding. In this paper, we introduce a JND (Just Noticeable Difference) profile and analyze how this profile characterizes rate and distortion. Based on JND profile, we devise a perceptual rate distortion (PRD) model with low-complexity to replace the Lagrange RD cost model. We scheme a novel inter macro-block partitioning method based on this PRD model. Our model and method first adopt perceptual filters to find the best mode of every macro-block and then start the motion estimation. Simulation results demonstrated that the inter mode decision based on PRD model won about 80% encoding time reduction and better quality than the inter mode decision based on the RD cost value decision.

Object categorization plays an important role in computer vision, semantic based image content understanding, and image retrieval. Wavelet packet transform provides a very good observation for the images by sub-band filtering. Different objects have distinctive characteristics in the sub-bands of wavelet packets, which should be discriminative for objects classification. In this paper, an object categorization method using hierarchical wavelet packet texture descriptors is proposed. Comparisons between Gabor texture descriptor, pyramid of histograms of orientation gradients (PHOG) and the proposed hierarchical wavelet packet texture descriptors on the widely used OT, Scene-13 and Sport event datasets are also given. Experimental results show that object categorization performances of the proposed texture descriptors are better than that of Gabor texture descriptor and as good as that of PHOG shape descriptor. Object categorization performances of the texture descriptors under various decomposition levels and wavelet bases are discussed. Performances of texture descriptors of global and local images with different partition patterns are also analyzed.

Parts of compressed video streams may be lost or corrupted when being transmitted over bandwidth limited networks and wireless communication networks with error-prone channels. Error concealment (EC) techniques are often adopted at the decoder side to improve the quality of the reconstructed video. Under the conditions of a high rate of data packets that arrives at the decoder corrupted, it is likely that the incorrectly decoded macro-blocks (MBs) are concentrated in a connected region, where important spatial reference information is lost. The conventional EC methods usually carry out the block concealment following a lexicographic scan (from top to bottom and from left to right of the image), which would make the methods ineffective for the case that the corrupted blocks are grouped in a connected region. In this paper, a temporal error concealment method, adaptive error concealment order determination (AECOD), is proposed to recover connected corrupted regions. The processing order of an MB in a connected corrupted region is adaptively determined by analyzing the external boundary patterns of the MBs in its neighborhood. The performances, on several video sequences, of the proposed EC scheme have been compared with those obtained by using other error concealment methods reported in the literature. Experimental results show that the AECOD algorithm can improve the recovery performance with respect to the other considered EC methods.

Error concealment (EC) techniques are often adopted at decoder side to improve the reconstructed video qualities. In this paper, a texture based selective boundary matching algorithm (TSBMA) is proposed to conceal erroneous macro-blocks (EMBs). Different from the conventional boundary matching algorithms which are carried out progressively in the raster scan mode, the processing order of the TSBMA for an EMB in a connected EMBs region is determined by the texture intensity information of its neighboring blocks, which are correct or recovered. The EMB with maximum texture intensity in its neighbors are firstly recovered. It is likely that the EMB which has more corrected MBs in its neighbors with stronger texture intensity has higher possibility to be recovered accurately, which will provide valuable information to recover the left EMBs in its neighbors. Thus, the proposed TSBMA can avoid the dependent recovery problem in the conventional BMAs. Moreover, the influence of boundary width and confidences of boundaries on the recovery results are discussed. Comparison results with different error concealment methods on several video sequences show the effectiveness of the proposed TSBMA.