List of Projects
In this work, we propose two novel forms of word image semantic representations. The first form learns an inflection invariant representation, thereby focusing on the root of the word, while the second form is built along the lines of textual word embedding techniques such as Word2Vec. We observe that such representations are useful for both traditional word spotting and also enrich the search results by accounting the semantic nature of the task.
People Involved : Sudhir Yarram, Girish Varma and C. V. Jawahar
Road networks in cities are massive and is a critical component of mobility. Fast response to defects, that can occur not only due to regular wear and tear but also because of extreme events like storms, is essential. Hence there is a need for an automated system that is quick, scalable and cost- effective for gathering information about defects. We propose a system for city-scale road audit, using some of the most recent developments in deep learning and semantic segmentation. For building and benchmarking the system, we curated a dataset which has annotations required for road defects. However, many of the labels required for road audit have high ambiguity which we overcome by proposing a label hierarchy
We consider the task of learning to estimate human pose in still images. In order to avoid the high cost of full supervision, we propose to use a diverse data set, which consists of two types of annotations: (i) a small number of images are labeled using the expensive ground-truth pose; and (ii) other images are labeled using the inexpensive action label. As action information helps narrow down the pose of a human, we argue that this approach can help reduce the cost of training without significantly affecting the accuracy.
Lecture videos are rich with textual information and to be able to understand the text is quite useful for larger video understanding/analysis applications. Though text recognition from images have been an active research area in computer vision, text in lecture videos has mostly been overlooked. In this work, we investigate the efficacy of state-of-the art handwritten and scene text recognition methods on text in lecture videos
Handwriting recognition (HWR) in Indic scripts is a challenging problem due to the inherent subtleties in the scripts, cursive nature of the handwriting and similar shape of the characters. Lack of publicly available handwriting datasets in Indic scripts has affected the development of handwritten word recognizers. In order to help resolve this problem, we release 2 handwritten word datasets: IIIT-HW-Dev, a Devanagari dataset and IIIT-HW-Telugu, a Telugu dataset.
Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos.
In this paper, we demonstrate a score based indexing approach for tennis videos. Given a broadcast tennis video (BTV), we index all the video segments with their scores to create a navigable and searchable match. Our approach temporally segments the rallies in the video and then recognizes the scores from each of the segments, before refining the scores using the knowledge of the tennis scoring system
Our goal is to spot words in silent speech videos without explicitly recognizing the spoken words, where the lip motion of the speaker is clearly visible and audio is absent. Existing work in this domain has mainly focused on recognizing a fixed set of words in word-segmented lip videos, which limits the applicability of the learned model due to limited vocabulary and high dependency on the model's recognition performance.
We propose a deep convolutional neural network named HWNet v2 (successor to our earlier work ) for the task of learning efficient word level representation for handwritten documents which can handle multiple writers and is robust to common forms of degradation and noise. We also show the generic nature of our representation and architecture which allows it to be used as off-the-shelf features for printed documents and building state of the art word spotting systems for various languages.
Building robust text recognition systems for languages with cursive scripts like Urdu has always been challenging. Intricacies of the script and the absence of ample annotated data further act as adversaries to this task. We demonstrate the effectiveness of an end-to-end trainable hybrid CNN-RNN architecture in recognizing Urdu text from printed documents, typically known as Urdu OCR. The solution proposed is not bounded by any language specific lexicon with the model following a segmentation-free, sequence-tosequence transcription approach. The network transcribes a sequence of convolutional features from an input image to a sequence of target labels.
Building robust recognizers for Arabic has always been challenging. We demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes. We outperform previous state-of-the-art on two publicly available video text datasets - ALIF and AcTiV. For the scene text recognition task, we introduce a new Arabic scene text dataset and establish baseline results. For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data. We overcome this by synthesizing millions of Arabic text images from a large vocabulary of Arabic words and phrases.
Person recognition methods that use multiple body regions have shown significant improvements over traditional face-based recognition. One of the primary challenges in full-body person recognition is the extreme variation in pose and view point. In this work, (i) we present an approach that tackles pose variations utilizing multiple models that are trained on specific poses, and combined using pose-aware weights during testing. (ii) For learning a person representation, we propose a network that jointly optimizes a single loss over multiple body regions. (iii) Finally, we introduce new benchmarks to evaluate person recognition in diverse scenarios and show significant improvements over previously proposed approaches on all the benchmarks including the photo album setting of PIPA.
We propose a deep convolutional feature representation that achieves superior performance for word spotting and recognition for handwritten images. We focus on :- (i) enhancing the discriminative ability of the convolutional features using a reduced feature representation that can scale to large datasets, and (ii) enabling query-by-string by learning a common subspace for image and text using the embedded attribute framework. We present our results on popular datasets such as the IAM corpus and historical document collections from the Bentham and George Washington pages.
We address the problem of predicting similarity between a pair of handwritten document images written by different individuals. This has applications related to matching and mining in image collections containing handwritten content. A similarity score is computed by detecting patterns of text re-usages between document images irrespective of the minor variations in word morphology, word ordering, layout and paraphrasing of the content.
We present an approach for analyzing the visual aesthetic property of a handwritten document page which matches with human perception. We formulate the problem at two independent levels: (i) coarse level which deals with the overall layout, space usages between lines, words and margins, and (ii) fine level, which analyses the construction of each word and deals with the aesthetic properties of writing styles. We present our observations on multiple local and global features which can extract the aesthetic cues present in the handwritten documents.
An exemplar based approach to detect the facial landmarks. We show that by using a very simple SIFT and HOG based descriptor, it is possible to identify the most accurate fiducial outputs from a set of results produced by regression and mixture of trees based algorithms (which we call candidate algorithms) on any given test image. Our approach manifests as two algorithms, one based on optimizing an objective function with quadratic terms and the other based on simple kNN.
A semi-supervised self-training method for fine-tuning human pose estimations in videos that provides accurate estimations even for complex sequences.
Generation of human like natural descriptions for multimedia content pose an interesting challenge for vision community. In our current work we tackle the challenge of generating descriptions for the videos. The proposed method demonstrates considerable success in generating syntactically and pragmatically correct text for lawn tennis videos and is notably effective in capturing majority of the video content. Unlike any previous work our method focuses on generating exhaustive and richer human like descriptions. We aim to provide reliable descriptions that facilitate the task of video analysis and help understand the ongoing events in the video. Large volumes of text data are used to compute associated text statistics which is thereafter used along with computer vision algorithms to produce relevant descriptions
Our aim is to learn relative attributes using local parts that are shared across categories. First, instead of using a global representation, we introduce a part-based representation combining a pair of images that specifically compares corresponding parts. Then, with each part we associate a locally adaptive “significance coefficient” that represents its discriminative ability with respect to a particular attribute. For each attribute, the significance-coefficients are learned simultaneously with a max-margin ranking model in an iterative manner. Compared to the baseline method , the new method is shown to achieve significant improvements in relative attribute prediction accuracy. Additionally, it is also shown to improve relative feedback based interactive image search.
We aim to decompose a global histogram representation of an image into histograms of its associated objects and regions. This task is formulated as an optimization problem, given a set of linear classifiers, which can effectively discriminate the object categories present in the image. Our decomposition bypasses harder problems associated with accurately localizing and segmenting objects.
Action recognition has gained significant attention from the computer vision community in recent years. This is a challenging problem, mainly due to the presence of significant camera motion, viewpoint transitions, varying illumination conditions and cluttered backgrounds in the videos. A wide spectrum of features and representations has been used for action recognition in the past. Recent advances in action recognition are propelled by (i) the use of local as well as global features, which have significantly helped in object and scene recognition, by computing them over 2D frames or over a 3D video volume (ii) the use of factorization techniques over video volume tensors and defining similarity measures over the resulting lower dimensional factors. In this project, we try to take advantages of both these approaches by defining a canonical correlation kernel that is computed from tensor representation of the videos. This also enables seamless feature fusion by combining multiple feature kernels.
In many real-life scenarios, an object can be categorized into multiple categories. E.g., a newspaper column can be tagged as "political", "election", "democracy"; an image may contain "tiger", "grass", "river"; and so on. These are instances of multi-label classification, which deals with the task of associating multiple labels with single data. Automatic image annotation is a multi-label classification problem that aims at associating a set of text with an image that describes its semantics.
Scene text recognition has gained significant attention from the computer vision community in recent years. Often images contain text which gives rich and useful information about their content. Recognizing such text is a challenging problem, even more so than the recognition of scanned documents. Scene text exhibits a large variability in appearances, and can prove to be challenging even for the state-of-the-art OCR methods. Many scene understanding methods recognize objects and regions like roads, trees, sky etc in the image successfully, but tend to ignore the text on the sign board. Our goal is to fill this gap in understanding the scene.
People Involved : Rahul Anand Sharma, Pramod Sankar K, C. V. Jawahar