Abstract

In this paper, we empirically study the performance of a set of pattern classification schemes for character classification problems. We argue that with a rich feature space, this class of problems can be solved with reasonable success using a set of statistical feature extraction schemes. Experimental validation is done on a data set (of more than 5,00,000 characters) collected and annotated from books printed primarily in Malayalam. Scope of this study include (a) applicability of a spectrum of classifiers and features (b) scalability of classifiers (c) sensitivity of features to degradation (d) generalization across fonts and (e) applicability across scripts

Abstract

Contemporary approaches to semantic indexing for bag of words image retrieval do not adapt well when the image or video collections dynamically get modified. In this paper, We propose an on-line incremental semantic indexing scheme for image retrieval in dynamic image collections. Our main contributions are in the form of a method and a data structure that tackle representation of the term document matrix and on-line semantic indexing where the database changes. We introduce a bipartite graph model (BGM) which is a scalable data structure that aids in online semantic indexing. It can also be incrementally updated. BGM uses tf-idf values for building a semantic bipartite graph. We also introduce a cash flow algorithm that works on the BGM to retrieve semantically relevant images from the database. We examine the properties of both BGM and cash flow algorithm through a series of experiments. Finally, we demonstrate how they can be effectively implemented to build large scale image retrieval systems in an incremental manner.

Abstract

Many of the successful multimedia retrieval systems focus on developing efficient and effective video retrieval solutions with the help of appropriate index structures. In these systems, the query is an example video and the retrieved results are similar video clips which are available apriori in the database. In this paper, we address a complementary problem of filtering a video stream based on a set of given examples. By filtering, we mean to detect, accept or reject the part of a video stream matching any of the given examples. This requires matching of example videos with the on-line video stream. Since the concepts of interest could be complex, we avoid explicit learning of a representation from the example videos to characterize the visual event present in the examples. We model the problem as simultaneous on-line spotting of multiple examples in a video stream. We employ a vocabulary trie for the filtering purpose and demonstrate the applicability of the technique in a variety of situations.

Abstract

This paper presents an XML-based scheme for managing a large multilingual OCR project. In particular we describe how a new XML based tagging scheme has been exploited to achieve the objectives of the project. Managing a large multi-lingual OCR project involving multiple research groups, developing script specific and script independent technologies in a collaborative fashion is a challenging problem. In this paper, we present some of the software and data management strategies designed for the project aimed at developing OCR for 11 scripts of Indian origin for which mature OCR technology was not available.

Abstract

The word error rate of any optical character recognition system (OCR) is usually substantially below its component or character error rate. This is especially true of Indic languages in which a word consists of many components. Current OCRs recognize each character or word separately and do not take advantage of document level constraints. We propose a document level OCR which incorporates information from the entire document to reduce word error rates. Word images are first clustered using a locality sensitive hashing technique. Individual words are then recognized using a (regular) OCR. The OCR outputs of word images in a cluster are then corrected probabilistically by comparing with the OCR outputs of other members of the same cluster. The approach may be applied to improve the accuracy of any OCR run on documents in any language. In particular, we demonstrate it for Telugu, where the use of language models for post-processing is not promising. We show a relative improvement of 28% for long words and 12% for all words which appear at least twice in the corpus.

Abstract

A standard solution for aligning scripts to movies is to use dynamic time warping with the subtitles (Everingham et al., BMVC 2006). We investigate the problem of aligning scripts to TV video/movies in cases where subtitles are not available, e.g. in the case of silent films or for film passages which are non-verbal. To this end we identify a number of “modes of alignment” and train classifiers for each of these. The modes include visual features, such as locations and face recognition, and audio features such as speech. In each case the feature gives some alignment information, but is too noisy when used independently. We show that combining the different features into a single cost function and optimizing this using dynamic programming, leads to a performance superior to each of the individual features. The method is assessed on episodes from the situation comedy Seinfeld, and on Charlie Chaplin and Indian movies.

Abstract

. In this paper, we propose a convex optimization based approach for piecewise planar reconstruction. We show that the task of reconstructing a piecewise planar environment can be set in an L∞ based Homographic framework that iteratively computes scene plane and camera pose parameters. Instead of image points, the algorithm optimizes over inter-image homographies. The resultant objective functions are minimized using Second Order Cone Programming algorithms. Apart from showing the convergence of the algorithm, we also empirically verify its robustness to error in initialization through various experiments on synthetic and real data. We intend this algorithm to be in between initialization approaches like decomposition methods and iterative non-linear minimization methods like Bundle Adjustment.

Abstract

Content based image retrieval (CBIR) has been well studied in the computer vision and multimedia community. Content free image retrieval (CFIR) methods, and their complementary characteristics to CBIR has not received enough attention in the literature. Performance of CBIR is constrained by the semantic gap between the feature representations and user expectations, while CFIR suffers with sparse logs and cold starts. We fuse both of them in a Bayesian framework to design a hybrid image retrieval system by overcoming their shortcomings. We validate our ideas and report experimental results, both qualitatively and quantitatively. We use our indexing scheme to efficiently represent both features and logs, thereby enabling scalability to millions of images.

Abstract

In this paper we present a statistical transliteration technique that is language independent. This technique uses statistical alignment models and Conditional Random Fields (CRF). Statistical alignment models maximizes the probability of the observed (source, target) word pairs using the expectation maximization algorithm and then the character level alignments are set to maximum posterior predictions of the model. CRF has efficient training and decoding processes which is conditioned on both source and target languages and produces globally optimal solution.

Abstract

Retrieving answer containing passages is a challenging task in Question Answering. In this paper we describe a novel query expansion method which aims to rank the answer containing passages better. It uses content and structured information (link structure and category information) of Wikipedia to generate a set of terms semantically related to the question. As Boolean model allows a fine-grained control over query expansion, these semantically related terms are added to the original query to form an expanded Boolean query. We conducted experiments on TREC 2006 QA data. The experimental results show significant improvements of about 24.6%, 11.1% and 12.4% in precision at 1, MRR at 20 and TDRR scores respectively using our query expansion method.