Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model

In today's technological era, document images play an important and integral part in our day to day life, and specifically with the surge of Covid-19, digitally scanned documents have become key source of communication, thus avoiding any sort of infection through physical contact. Storage and transmission of scanned document images is a very memory intensive task, hence compression techniques are being used to reduce the image size before archival and transmission. To extract information or to operate on the compressed images, we have two ways of doing it. The first way is to decompress the image and operate on it and subsequently compress it again for the efficiency of storage and transmission. The other way is to use the characteristics of the underlying compression algorithm to directly process the images in their compressed form without involving decompression and re-compression. In this paper, we propose a novel idea of developing an OCR for CCITT (The International Telegraph and Telephone Consultative Committee) compressed machine printed TIFF document images directly in the compressed domain. After segmenting text regions into lines and words, HMM is applied for recognition using three coding modes of CCITT- horizontal, vertical and the pass mode. Experimental results show that OCR on pass modes give a promising results.Comment: The paper has 14 figures and 1 tabl

Visual Representation of Text in Web Documents and Its Interpretation

This paper examines the uses of text and its representation on Web documents in terms of the challenges in its interpretation. Particular attention is paid to the significant problem of non-uniform representation of text. This non-uniformity is mainly due to the presence of semantically important text in image form as opposed to the standard encoded text. The issues surrounding text representation in Web documents are discussed in the context of colour perception and spatial representation. The characteristics of the representation of text in image form are examined and research towards interpreting these images of text is briefly described

Visual Representation of Text in Web Documents and Its Interpretation

This paper examines the uses of text and its representation on Web documents in terms of the challenges in its interpretation. Particular attention is paid to the significant problem of non-uniform representation of text. This non-uniformity is mainly due to the presence of semantically important text in image form as opposed to the standard encoded text. The issues surrounding text representation in Web documents are discussed in the context of colour perception and spatial representation. The characteristics of the representation of text in image form are examined and research towards interpreting these images of text is briefly described

T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network

The problem of generating textual descriptions for the visual data has gained research attention in the recent years. In contrast to that the problem of generating visual data from textual descriptions is still very challenging, because it requires the combination of both Natural Language Processing (NLP) and Computer Vision techniques. The existing methods utilize the Generative Adversarial Networks (GANs) and generate the uncompressed images from textual description. However, in practice, most of the visual data are processed and transmitted in the compressed representation. Hence, the proposed work attempts to generate the visual data directly in the compressed representation form using Deep Convolutional GANs (DCGANs) to achieve the storage and computational efficiency. We propose GAN models for compressed image generation from text. The first model is directly trained with JPEG compressed DCT images (compressed domain) to generate the compressed images from text descriptions. The second model is trained with RGB images (pixel domain) to generate JPEG compressed DCT representation from text descriptions. The proposed models are tested on an open source benchmark dataset Oxford-102 Flower images using both RGB and JPEG compressed versions, and accomplished the state-of-the-art performance in the JPEG compressed domain. The code will be publicly released at GitHub after acceptance of paper.Comment: Accepted for publication at IAPR's 6th CVIP 202

Information extraction from multimedia web documents: an open-source platform and testbed

The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval