Lazy User Behaviour

In this position paper we suggest that a user will most often choose the solution (device) that will fulfill her (information) needs with the least effort. We call this “lazy user behavior”. We suggest that the principle components responsible for solution selection are the user need and the user state. User need is the user’s detailed (information) need (urgency, type, depth, etc.) and user state is the situation, in which the user is at the moment of the need (location, time, etc.); the user state limits the set of available solutions (devices) to fulfill the user need. The context of this paper is the use of mobile devices and mobile services. We present the lazy user theory of solution selection, two case examples, and discuss the implications of lazy user behavior on user attachment to mobile services and devices, and to planning and execution of mobile services.User Attachment; Lazy User; Mobile Services; Mobile Devices; Adoption; Acceptance; Least Effort

Keyword and Image Content Features for Image Indexing and Retrieval Within Compressed Domain

The central problem of most Content Based Image Retrieval approaches is poor quality in terms of sensitivity (recall) and specificity (precision). To overcome this problem, the semantic gap between high-level concepts and low-level features has been acknowledged. In this paper we introduce an approach to reduce the impact of the semantic gap by integrating high-level (semantic) and low-level features to improve the quality of Image Retrieval queries. Our experiments have been carried out by applying two hierarchical procedures. The first approach is called keyword-content, and the second content-keyword. Our proposed approaches show better results compared to a single method (keyword or content based) in term of recall and precision. The average precision has increased by up to 50%

KEYWORD AND IMAGE CONTENT FEATURES FOR IMAGE INDEXING AND RETRIEVAL WITHIN COMPRESSED DOMAIN

The central problem of most Content Based Image Retrieval approaches is poor quality in terms of sensitivity (recall) and specificity (precision). To overcome this problem, the semantic gap between high-level concepts and low-level features has been acknowledged. In this paper we introduce an approach to reduce the impact of the semantic gap by integrating high-level (semantic) and low-level features to improve the quality of Image Retrieval queries. Our experiments have been carried out by applying two hierarchical procedures. The first approach is called keyword-content, and the second content-keyword. Our proposed approaches show better results compared to a single method (keyword or content based) in term of recall and precision. The average precision has increased by up to 50%

A Probabilistic Multimedia Retrieval Model and its Evaluation

We present a probabilistic model for the retrieval of multimodal documents. The model is based on Bayesian decision theory and combines models for text-based search with models for visual search. The textual model is based on the language modelling approach to text retrieval, and the visual information is modelled as a mixture of Gaussian densities. Both models have proved successful on various standard retrieval tasks. We evaluate the multimodal model on the search task of TREC′s video track. We found that the disclosure of video material based on visual information only is still too difficult. Even with purely visual information needs, text-based retrieval still outperforms visual approaches. The probabilistic model is useful for text, visual, and multimedia retrieval. Unfortunately, simplifying assumptions that reduce its computational complexity degrade retrieval effectiveness. Regarding the question whether the model can effectively combine information from different modalities, we conclude that whenever both modalities yield reasonable scores, a combined run outperforms the individual runs

Lazy User Behaviour

In this position paper we suggest that a user will most often choose the solution (device) that will fulfill her (information) needs with the least effort. We call this “lazy user behavior”. We suggest that the principle components responsible for solution selection are the user need and the user state. User need is the user’s detailed (information) need (urgency, type, depth, etc.) and user state is the situation, in which the user is at the moment of the need (location, time, etc.); the user state limits the set of available solutions (devices) to fulfill the user need. The context of this paper is the use of mobile devices and mobile services. We present the lazy user theory of solution selection, two case examples, and discuss the implications of lazy user behavior on user attachment to mobile services and devices, and to planning and execution of mobile services

Lazy User Behaviour

In this position paper we suggest that a user will most often choose the solution (device) that will fulfill her (information) needs with the least effort. We call this “lazy user behavior”. We suggest that the principle components responsible for solution selection are the user need and the user state. User need is the user’s detailed (information) need (urgency, type, depth, etc.) and user state is the situation, in which the user is at the moment of the need (location, time, etc.); the user state limits the set of available solutions (devices) to fulfill the user need. The context of this paper is the use of mobile devices and mobile services. We present the lazy user theory of solution selection, two case examples, and discuss the implications of lazy user behavior on user attachment to mobile services and devices, and to planning and execution of mobile services

RAM: array processing over a relational DBMS

Developing multimedia applications in relational databases is hindered by a mismatch in computational frameworks. Efficient manipulation of multimedia data calls for array-based processing, which at best is available as a database add-on, not supported by the query optimizer. As a result, array-based processing ends up in dedicated programs outside the DBMS: non-reusable black boxes. The goal of our research is to reduce this gap between user-needs and system functionality by developing a seemless integration of array processing in a relational algebra engine. The paper introduces a declarative language for array-expressions based on the array comprehension, and its mapping to a relational kernel in a prototype implementation. The layered architecture of the resulting array database management system allows the use of structural knowledge available in the array data type. This additional source of information can be exploited for query optimization, which is demonstrated with a case study. The experiments show how the performance of a standard tool for matrix computations can be achieved without sacrificing data independence, highlighting however a critical aspect in the DBMS architecture proposed

Discrete language models for video retrieval

Finding relevant video content is important for producers of television news, documentanes and commercials. As digital video collections become more widely available, content-based video retrieval tools will likely grow in importance for an even wider group of users. In this thesis we investigate language modelling approaches, that have been the focus of recent attention within the text information retrieval community, for the video search task. Language models are smoothed discrete generative probability distributions generally of text and provide a neat information retrieval formalism that we believe is equally applicable to traditional visual features as to text. We propose to model colour, edge and texture histogrambased features directly with discrete language models and this approach is compatible with further traditional visual feature representations. We provide a comprehensive and robust empirical study of smoothing methods, hierarchical semantic and physical structures, and fusion methods for this language modelling approach to video retrieval. The advantage of our approach is that it provides a consistent, effective and relatively efficient model for video retrieval