List of Projects
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 We present our results on popular datasets such as the IAM corpus and historical document collections from the Bentham and George Washington pages.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