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To improve the performance of sketch-based image retrieval (SBIR) methods, most existing SBIR methods develop brand new SBIR methods. In fact, a re-ranking approach, which can refine the retrieval results of SBIR methods, is also beneficial. Inspired by this, in this paper, an SBIR re-ranking approach based on multi-clustering is proposed. In order to make the re-ranking approach invisible to users and adaptive to different types of image datasets, we made it an unsupervised method using blind feedback. Distinguished from the existing methods, this re-ranking approach uses the semantic information of three types of images: edge maps, object images (images with black background and natural images’ foreground objects) and natural images themselves. With the initial retrieval results of an SBIR method, our approach first does the clustering operation for three types of images. Then, we utilize the clustering results to generate a cluster score for each initial retrieval result. Finally, the cluster score is used to calculate the final retrieval scores for the initial retrieval results. The experiments on different SBIR datasets are conducted. Experimental results demonstrate that, by implementing our re-ranking approach, the retrieval accuracy of a variety of SBIR methods is increased. Furthermore, the comparisons between our re-ranking method and the existing re-ranking methods are given. [ABSTRACT FROM AUTHOR]

Place-of-Interest (POI) summarization by aesthetics evaluation can recommend a set of POI images to the user and it is significant in image retrieval. In this paper, we propose a system that summarizes a collection of POI images regarding both aesthetics and diversity of the distribution of cameras. First, we generate visual albums by a coarse-to-fine POI clustering approach and then generate 3D models for each album by the collected images from social media. Second, based on the 3D to 2D projection relationship, we select candidate photos in terms of the proposed crowd source saliency model. Third, in order to improve the performance of aesthetic measurement model, we propose a crowd-sourced saliency detection approach by exploring the distribution of salient regions in the 3D model. Then, we measure the composition aesthetics of each image and we explore crowd source salient feature to yield saliency map, based on which, we propose an adaptive image adoption approach. Finally, we combine the diversity and the aesthetics to recommend aesthetic pictures. Experimental results show that the proposed POI summarization approach can return images with diverse camera distributions and aesthetics. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

Wavelet packet transform is an effective texture analysis approach by sub-band filtering. Different texture patterns have distinctive responses to the sub-bands of wavelet packets. The responses are valuable for texture description. Utilizing all the responses of the sub-bands of different resolutions can improve texture pattern discrimination power. In this paper, effective texture descriptors based on hierarchical wavelet packet (HWVP) transform are proposed. The subtle sub-bands of wavelet packet transform improve the discrimination power of HWVP descriptors for the images in different categories. Scene categorization performances of the HWVP descriptors under various decomposition levels and wavelet bases are discussed. Performances of HWVP descriptors of global and local images with different partition patterns are also analyzed. The advantages of HWVP descriptors attribute to the following two aspects. Firstly sub-band filtering is helpful for improving the discrimination power of HWVP descriptors to capture the subtle differences of texture patterns. Secondly hierarchical feature representation makes the HWVP descriptors robust to resolution variations. Comparisons are made with some existing robust descriptors on scene categorization and semantic concept retrieval. Experimental results on the widely used OT, Scene-13, Sport Event, and TRECVID 2007 datasets show the effectiveness of the proposed HWVP descriptors. [ABSTRACT FROM AUTHOR]

Abstract: Online social image share websites such as Flickr and Panoramio allow users to manually annotate their images with their own words, which can be used to facilitating image retrieval and other image applications. The smart-phones have made it possible for users to capture images as well as get the geographical ordinates. It is easily recognized and accepted that visually similar images captured in the same place or in the same period of time may be also relevant in contents. In this paper we propose a personalized photo tagging approach by using users'' own vocabularies. It can recommend users preferred tags for their newly uploaded photos based on the history information in their social communities by modeling users'' tagging habit. The fundamental idea of our approach is that we try to recommend tags to users by accumulating votes from the candidate images. The candidate images are selected in term of three factors: visual features, geographical coordinates and image taken time. Thus, the candidate images include visually similar images, images captured in the same geographical coordinates or in the same period of time. Based on these three factors, we implement seven experiments. Experimental results on a Flickr image collection of nearly 2 million images of 5607 users demonstrate the effectiveness of our approach. The experimental comparison shows that the three factors have certain effectiveness in image tagging. The image tagging approach by fusing the image taken time, GPS information, and visual features achieve satisfactory performance . The impacts of history information and the batch tagging behavior to the image tagging performances are discussed. [Copyright &y& Elsevier]