With rapid growth of LTE network and Voice-over-LTE(VoLTE), detecting and preventing security threats like Denial of Service attack becomes a necessary and urgent requirement. VoLTE is an voice solution based on Internet Protocol and 4G LTE technology, at the same time exposing many vulnerabilities when using packet-switched network. There are many heavy weighted detection systems using content analysis, while high demands of computing resource constraint their practical use. In this paper, we purpose a lightweight detection scheme for VoLTE network security, based on analysis of data traffic flow. To optimize parameters in our scheme, we formulate a Bayesian game model. Bayesian game has the features of incomplete information and asymmetry, similar to practical attack-defense model. Besides, The dynamic Bayesian game is more realistic, since both sides can update believes about their opponents. Simulation results provide some guidances on parameter selection, as well as verifying the superiority of our scheme.

The processing speed for positron emission tomography (PET) image reconstruction has been greatly improved in recent years by simply dividing the workload to multiple processors of a graphics processing unit (GPU). However, if this strategy is generalized to a multi-GPU cluster, the processing speed does not improve linearly with the number of GPUs. This is because large data transfer is required between the GPUs after each iteration, effectively reducing the parallelism. This paper proposes a novel approach to reformulate the maximum likelihood expectation maximization (MLEM) algorithm so that it can scale up to many GPU nodes with less frequent inter-node communication. While being mathematically different, the new algorithm maximizes the same convex likelihood function as MLEM, thus converges to the same solution. Experiments on a multi-GPU cluster demonstrate the effectiveness of the proposed approach.

Web-scale image search engines (e.g., Google image search, Bing image search) mostly rely on surrounding text features. It is difficult for them to interpret users' search intention only by query keywords and this leads to ambiguous and noisy search results which are far from satisfactory. It is important to use visual information in order to solve the ambiguity in text-based image retrieval. In this paper, we propose a novel Internet image search approach. It only requires the user to click on one query image with minimum effort and images from a pool retrieved by text-based search are reranked based on both visual and textual content. Our key contribution is to capture the users' search intention from this one-click query image in four steps. 1) The query image is categorized into one of the predefined adaptive weight categories which reflect users' search intention at a coarse level. Inside each category, a specific weight schema is used to combine visual features adaptive to this kind of image to better rerank the text-based search result. 2) Based on the visual content of the query image selected by the user and through image clustering, query keywords are expanded to capture user intention. 3) Expanded keywords are used to enlarge the image pool to contain more relevant images. 4) Expanded keywords are also used to expand the query image to multiple positive visual examples from which new query specific visual and textual similarity metrics are learned to further improve content-based image reranking. All these steps are automatic, without extra effort from the user. This is critically important for any commercial web-based image search engine, where the user interface has to be extremely simple. Besides this key contribution, a set of visual features which are both effective and efficient in Internet image search are designed. Experimental evaluation shows that our approach significantly improves the precision of top-ranked images and also the user experience.

We propose three innovative interactive methods to let computer better understand user intention in content-based image retrieval: 1. Smart intention list induces user intention, thereby improves search results by intention-specific search schema; 2. Reference strokes interaction allows user to specify in detail about the intention by pointing out interested regions; 3. Natural user feedback easily collects data of user relevance feedbacks to boost the performance of the system. Systematic user study shows that the proposed interactive mechanism improves search efficiency, reduces user workload, and enhances user experience.

In this paper, we address the issue of transducing the object cutout model from an example image to novel image instances. We observe that although object and background are very likely to contain similar colors in natural images, it is much less probable that they share similar color configurations. Motivated by this observation, we propose a local color pattern model to characterize the color configuration in a robust way. Additionally, we propose an edge profile model to modulate the contrast of the image, which enhances edges along object boundaries and attenuates edges inside object or background. The local color pattern model and edge model are integrated in a graph-cut framework. Higher accuracy and improved robustness of the proposed method are demonstrated through experimental comparison with state-of-the-art algorithms.

We analyze the sub-optimality of traditional greedy active learning based relevance feedback methods in image retrieval, and propose a novel active learning approach to query labels of multiple images together, which minimize the needed round of feedbacks and achieve satisfactory result in a near optimal manner. Our experiments on real image retrieval demonstrate that our solution can yield comparable precession/recall rate by significantly less relevance feedbacks.

Nowadays, web-scale image search engines (e.g. Google, Live Image Search) rely almost purely on surrounding text features. This leads to ambiguous and noisy results. We propose to use adaptive visual similarity to re-rank the text-based search results. A query image is first categorized into one of several predefined intention categories, and a specific similarity measure is used inside each category to combine image features for re-ranking based on the query image. Extensive experiments demonstrate that using this algorithm to filter output of Google and Live Image Search is a practical and effective way to dramatically improve the user experience. A real-time image search engine is developed for on-line image search with re-ranking: http://mmlab.ie.cuhk.edu.hk/intentsearch

In this demo, we present IntentSearch, an interactive system for realtime web based image retrieval. IntentSearch works directly on top of Microsoft Live Image Search, and re-ranks its results according to user specified query image(s) and the automatically inferred user intention. Besides searching in the interface of Microsoft Live Image Search, we also design a more flexible interface to let users browse and play with all the images in the current search session, which makes web image search more efficient and interesting. Please visit http://mmlab.ie.cuhk.edu.hk/intentsearch for the experience.

Digital photo management is becoming indispensable for the explosively growing family photo albums due to the rapid popularization of digital cameras and mobile phone cameras. In an effective photo management system photo annotation is the most challenging task. In this paper, we develop several innovative interaction techniques for semi-automatic photo annotation. Compared with traditional annotation systems, our approach provides the following new features: "cluster annotation" puts similar faces or photos with similar scene together, and enables user label them in one operation; "contextual re-ranking" boosts the labeling productivity by guessing the user intention; "ad hoc annotation" allows user label photos while they are browsing or searching, and improves system performance progressively through learning propagation. Our results show that these technologies provide a more user friendly interface for the annotation of person name, location, and event, and thus substantially improve the annotation performance especially for a large photo album.

We propose a flexible interaction mechanism for CBIR by enabling relevance feedback inside images through drawling strokes. User's interest is obtained from an easy-to-use user interface, and fused seamlessly with traditional feedback information in a semi-supervised learning framework. Retrieval performance is boosted due to more precise description of the query concept. Region segmentation is also improved based on the collected strokes, and further enhances the retrieval precision. We implement our system Flexible Image Search Tool (FIST) based on the ideas above. Experiments on two real world data sets demonstrate the effectiveness of our approach.