15989010
Interactive Object-Based Retrieval Using Relevance Feedback
In this paper we present an interactive, object-based video retrieval system which features a novel query formulation method that is used to iteratively refine an underlying model of the search object. As the user continues query composition and browsing of retrieval results, the system’s object modeling process, based on Gaussian probability distributions, becomes incrementally more accurate, leading to better search results. To make the interactive process understandable and easy to use, a custom user-interface has been designed and implemented that allows the user to interact with segmented objects in formulating a query, in browsing a search result, and in re-formulating a query by selecting an object in the search result.
15223970
User Interface Issues for Browsing Digital Video
In this paper we examine a suite of systems for content-based indexing and browsing of digital video and we identify a superset of features and functions which are provided by these systems. From our classification of these we have identified that common to all is the fact of being predominantly technology-based, with little attention paid to actual user requirements. As part of our work we are developing an application for content-based browsing of digital video which will incorporate the most desirable but achievable of the functions of other systems. This will be achieved via a series of continuously refined demonstrator systems from Spring 1999 onwards which will be subjected to analysis of performance in terms of user.
15518000
MediAssist: Using Content-Based Analysis and Context to Manage Personal Photo Collections
We present work which organises personal digital photo collections based on contextual information, such as time and location, combined with content-based analysis such as face detection and other feature detectors. The MediAssist demonstration system illustrates the results of our research into digital photo management, showing how a combination of automatically extracted context and content-based information, together with user annotation, facilitates efficient searching of personal photo collections.
6879205
Mobile access to personal digital photograph archives
Handheld computing devices are becoming highly connected devices with high capacity storage. This has resulted in their being able to support storage of, and access to, personal photo archives. However the only means for mobile device users to browse such archives is typically a simple one-by-one scroll through image thumbnails in the order that they were taken, or by manually organising them based on folders. In this paper we describe a system for context-based browsing of personal digital photo archives. Photos are labeled with the GPS location and time they are taken and this is used to derive other context-based metadata such as weather conditions and daylight conditions. We present our prototype system for mobile digital photo retrieval, and an experimental evaluation illustrating the utility of location information for effective personal photo retrieval.
586486
Object-Based Access to TV Rushes Video
Recent years have seen the development of different modalities for video retrieval. The most common of these are (1) to use text from speech recognition or closed captions, (2) to match keyframes using image retrieval techniques like colour and texture [6] and (3) to use semantic features like “indoor”, “outdoor” or “persons”. Of these, text-based retrieval is the most mature and useful, while image-based retrieval using low-level image features usually depends on matching keyframes rather than whole-shots. Automatic detection of video concepts is receiving much attention and as progress is made in this area we will see consequent impact on the quality of video retrieval. In practice it is the combination of these techniques which realises the most useful, and effective, video retrieval as shown by us repeatedly in TRECVid [5].
5988668
Using text search for personal photo collections with the MediAssist system
The MediAssist system enables organisation and searching of personal digital photo collections based on contextual information, content-based analysis and semi-automatic annotation. One mode of user interaction uses automatically extracted features to create text surrogates for photos, which enables text search of photo collections without manual annotation. Our evaluation shows that this text search facility is effective for known-item search.
10735662
MAS platforms as an enabler of enterprise mobilisation: the state of the art
One of the main application areas for multi-agent systems technology is enterprise mobilization, wherein the main business process actors are nomadic workers. An agent's autonomy, sociality and intelligence are highly prized features when it comes to supporting those mobile workers who are geographically isolated from the main knowledge source (i.e. the corporate Intranet) and are frequently moving from one location to another. Based on experience gained from two field trials of applications (built using for multi-agent systems technology and running on lightweight handheld devices) that support mobile business processes for telecommunications service provisioning and maintenance, this paper proposes desirable metrics for any multi-agent systems platform intended for enterprise mobilisation use. These metrics are then used to compare a number of existing multi-agent systems platforms, and based on the results, this paper identifies some areas for improvement.
7431488
An HMM-Based Threshold Model Approach for Gesture Recognition
A new method is developed using the hidden Markov model (HMM) based technique. To handle nongesture patterns, we introduce the concept of a threshold model that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture patterns. The threshold model is a weak model for all trained gestures in the sense that its likelihood is smaller than that of the dedicated gesture model for a given gesture. Consequently, the likelihood can be used as an adaptive threshold for selecting proper gesture model. It has, however, a large number of states and needs to be reduced because the threshold model is constructed by collecting the states of all gesture models in the system. To overcome this problem, the states with similar probability distributions are merged, utilizing the relative entropy measure. Experimental results show that the proposed method can successfully extract trained gestures from continuous hand motion with 93.14% reliability.
214800963
High Efficiency Counter Mode Security Architecture via Prediction and Precomputation
Encrypting data in unprotected memory has gained much interest lately for digital rights protection and security reasons. Counter Mode is a well-known encryption scheme. It is a symmetric-key encryption scheme based on any block cipher, e.g. AES. The schemeýs encryption algorithm uses a block cipher, a secret key and a counter (or a sequence number) to generate an encryption pad which is XORed with the data stored in memory. Like other memory encryption schemes, this method suffers from the inherent latency of decrypting encrypted data when loading them into the on-chip cache. One solution that parallelizes data fetching and encryption pad generation requires the sequence numbers of evicted cache lines to be cached on-chip. On-chip sequence number caching can be successful in reducing the latency at the cost of a large area overhead. In this paper, we present a novel technique to hide the latency overhead of decrypting counter mode encrypted memory by predicting the sequence number and pre-computing the encryption pad that we call one-time-pad or OTP. In contrast to the prior techniques of sequence number caching, our mechanism solves the latency issue by using idle decryption engine cycles to speculatively predict and pre-compute OTPs before the corresponding sequence number is loaded. This technique incurs very little area overhead. In addition, a novel adaptive OTP prediction technique is also presented to further improve our regular OTP prediction and precomputation mechanism. This adaptive scheme is not only able to predict encryption pads associated with static and infrequently updated cache lines but also those frequently updated ones as well. Experimental results using SPEC2000 benchmark show an 82% prediction rate. Moreover, we also explore several optimization techniques for improving the prediction accuracy. Two specific techniques, Two-level prediction and Context-based prediction are presented and evaluated. For the two-level prediction, the prediction rate was improved from 82% to 96%. With the context-based prediction, the prediction rate approaches 99%. Context-based OTP prediction outperforms a very large 512KB sequence number cache for many memory-bound SPEC programs. IPC results show an overall 15% to 40% performance improvement using our prediction and precomputation, and another 7% improvement when context-based prediction techniques is used.
1174061
Combined QoS Scheduling and Call Admission Control Algorithm in Cellular Networks
In this paper, a scheduling problem in wireless networks is considered when there simultaneously exist CBR (constant bit rate) users requiring exact minimum average throughput guarantee and EMG (elastic with minimum guarantee) users requiring minimum average throughput guarantee and more if possible. By exploiting utility maximization problem without minimum throughput constraint and newly defined utility functions, we propose a combined scheduling and call admission control scheme that exactly guarantees the minimum requirements of CBR and EMG users and then allocates the leftover capacity to EMG users. In the proposed scheme, it is easy to give priority to particular users so that they are guaranteed their requirements prior to any other user. Moreover, the priority structure enables the proposed measurement-based call admission control algorithm to perform admission trial without affecting the minimum performance of existing users. We show through mathematical analysis and simulations that our scheme works as designed.
14075351
A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image
When we look at a picture, our prior knowledge about the world allows us to resolve some of the ambiguities that are inherent to monocular vision, and thereby infer 3d information about the scene. We also recognize different objects, decide on their orientations, and identify how they are connected to their environment. Focusing on the problem of autonomous 3d reconstruction of indoor scenes, in this paper we present a dynamic Bayesian network model capable of resolving some of these ambiguities and recovering 3d information for many images. Our model assumes a "floorwall" geometry on the scene and is trained to recognize the floor-wall boundary in each column of the image. When the image is produced under perspective geometry, we show that this model can be used for 3d reconstruction from a single image. To our knowledge, this was the first monocular approach to automatically recover 3d reconstructions from single indoor images.