Abstract

Retrieval and indexing in multimedia databases has been an active topic both in the Information Retrieval and computer vision communities for a long time. In this paper we propose a novel region based indexing and retrieval scheme for images. First we present our virtual textual description using which, images are converted to text documents containing keywords. Then we look at how these documents can be indexed and retrieved using modified elastic bucket tries and show that our approach is one order better than standard spatial indexing approaches. We also show various operations required for dealing with complex features like relevance feedback. Finally we analyze the method comparatively and and validate our approach.

Abstract

In this paper we propose a novel scheme to combine offline and online features of handwritten strokes. The stateof-the-art methods in handwritten stroke recognition have used a pre-determined combination of these features, which is not optimal in all situations. The proposed model addresses this issue by learning mixtures of offline and online characteristics from a set of exemplars. Each stroke is represented as a probabilistic sequence of substrokes with varying compositions of these features. The model adapts to any stroke and chooses the feature composition that best characterizes it. The superiority of the method is demonstrated on handwritten numeral and character strokes.

Abstract

In this paper, we present a new integration method for improving the performance of visual servoing. The method integrates both image-based visual servoing (IBVS) and positionbased visual servoing (PBVS) to satisfy the requirements of the visual servoing process. We define a probabilistic integration rule for IBVS and PBVS controllers. Density functions that determine the probability of each controller are defined to satisfy the above constraints. We prove that this integration method provides global stability, and avoids local minima. The new integration method is validated on positioning tasks, and compared with other switching methods. I. I

Abstract

Synthesis of handwriting has a variety of applications including generation of personalized documents, study of writing styles, automatic generation of data for training recognizers, and matching of handwritten data for retrieval. Most of the existing algorithms for handwriting synthesis deal with English, where the spatial layout of the components are relatively simple, while the cursiveness of the script introduces many challenges. In this paper, we present a synthesis model for generating handwritten data for Indian languages, where the layout of characters is complex while the script is fundamentally non-cursive. The algorithm learns from annotated data and improves its representation with feedback.

Abstract

In this paper we share the experience gained from establishing a process and a supporting architecture for the Digital Library of India (DLI) project. The DLI project was started with a vision of digitizing books and making them available online, in a searchable and browseable form. The digitization of the books takes place at geographically distributed locations. This raises many issues related to policy and collaboration. We discuss these problems in detail and present the process and workflow that is established to solve them. We also share the architecture of the project that supports the smooth implementation of the process. The architecture of the DLI project has been arrived at after considering factors like high performance, scalability, availability and economy.

Abstract

Relevance feedback in Content Based Image Retrieval(CBIR) has been an active field of research for quite some time now. Many schemes and techniques of relevance feedback exist with many assumptions and operating criteria. Yet there exist few ways of quantitatively measuring and comparing different relevance feedback algorithms. Such analysis is necessary if a CBIR system is to perform consistently. In this paper we propose an abstract model of a CBIR system where the effects of different modules over the entire system is observed. Using this model we thoroughly analyse performance a set of basic relevance feedback algorithms. Besides using standard measures like precision and recall we also suggest two new measures to gauge the performance of any contemporary CBIR system.

Abstract

In this paper we present a new integration method for improving the performance of visual servoing. The method integrates image-based visual servoing (IBVS) and positionbased visual servoing (PBVS) approaches to satisfy the widely varying requirements of the visual servoing process. We define an integration rule for IBVS and PBVS controllers. Density functions that determine the weighting factor of each controller are defined to satisfy the above constraints. We prove that this integration method provides global stability, and avoids local minima. The new integration method is validated on positioning tasks and compared with other switching methods.

Abstract

Many problems in computer vision such as pose recovery and structure estimation are formulated as a minimization process. These problems vary in the use of image measurements directly or using them to extract 3D cues in the minimization process. Hybrid methods have the advantage of combining the 2D and 3D visual information to improve the performance over the above two methods. In this paper, we present a new formulation for minimizing a class of hybrid error functions. This is done by using 2D information from the image space and 3D information from the Cartesian space in one error function. Applications to visual servoing and image alignment problems are presented. The positioning task of a robot arm has been formulated as a minimization problem. Gradient decent as a first order approxima ion and Gauss-Newton as a second order approximation are considered in this paper. Simulation results show, comparing with 2 1/2 D hybrid method, that these two\ methods provide an efficient solution to the features visibility problems and the camera trajectory in the Cartesian space.

Abstract

Today web search engines provide the easiest way to reach information on the web. In this scenario, more than 95% of Indian language content on the web is not searchable due to multiple encodings of web pages. Most of these encodings are proprietary and hence need some kind of standardization for making the content accessible via a search engine. In this paper we present a search engine called WebKhoj which is capable of searching multi-script and multi-encoded Indian language content on the web. We describe a language focused crawler and the transcoding processes involved to achieve accessibility of Indian langauge content. In the end we report some of the experiments that were conducted along with results on Indian language web content.

Abstract

The task in Document Understanding Conferences (DUC1) 2006 is to generate a fixed length, user oriented, multi document summary, which remains same as that of DUC 2005. We have used two features to score the sentences based. The sentences are picked to form the summary based on the calculated score. The first feature is a query dependent scoring of a sentence which is an improvement over the HAL feature. The second feature is based on the observation that sentence importance, which is independent of the query, needs to be captured in the current approaches. We have explored the use of web in scoring the sentences in a query independent manner. Experiments show a performance gain of 6-7% over HAL feature by the inclusion of two new features. Our summarization system was ranked 1st in all automatic evaluations with significant margin from second best system, 5th in responsiveness and 9th in linguistic quality evaluations in DUC 2006. Relatively lower performance in linguistic quality can be attributed to the stripped off sentences at the end of summary, when the summary length is exceeding 250 words.