Abstract

Typical retrieval systems have three requirements: a) Accurate retrieval, ie, the method should have high precision, b) Diverse retrieval, ie, the obtained set of samples should be diverse, and c) Retrieval time should be small. However, most of the existing methods address only one or two of the above mentioned requirements. In this work, we present a method based on randomized locality sensitive hashing which tries to address all of the above requirements simultaneously. While earlier hashing-based approaches considered approximate retrieval to be acceptable only for the sake of efficiency, we argue that one can further exploit approximate retrieval to provide impressive trade-offs between accuracy and diversity. We also extend our method to the problem of multi-label prediction, where the goal is to output a diverse and accurate set of labels for a given document in real-time. Finally, we present empirical …

Abstract

Robotic manipulation of objects in clutter remains a challenging problem to date. The challenge is posed by various levels of complexity involved in interaction among objects. Understanding these semantic interactions among different objects is important to manipulate in complex settings. It can play a significant role in extending the scope of manipulation to cluttered environment involving generic objects, and both direct and indirect physical contact. In our work, we aim at learning semantic interaction among objects of generic shapes and sizes lying in clutter involving physical contact. We infer three types of support relationships: “support from below”, “support from side”, and “containment”. Subsequently, the learned semantic interaction or support relationship is used to derive a sequence or order in which the objects surrounding the object of interest should be removed without causing damage to the …

Abstract

Generating synthetic images is an art which emulates the natural process of image generation in a closest possible manner. In this work, we exploit such a framework for data generation in handwritten domain. We render synthetic data using open source fonts and incorporate data augmentation schemes. As part of this work, we release 9M synthetic handwritten word image corpus which could be useful for training deep network architectures and advancing the performance in handwritten word spotting and recognition tasks.

Abstract

In multi-instance data, every object is a bag that contains multiple elements or instances. Each bag may be assigned to one or more classes, such that it has at least one instance corresponding to every assigned class. However, since the annotations are at bag-level, there is no direct association between the instances within a bag and the assigned class labels, hence making the problem significantly challenging. While existing methods have mostly focused on Bag-to-Bag or Class-to-Bag distances, in this paper, we address the multiple instance learning problem using a novel Bag-to-Class distance measure. This is based on two observations:(a) existence of outliers is natural in multi-instance data, and (b) there may exist multiple instances within a bag that belong to a particular class. In order to address these, in the proposed distance measure (a) we employ L1-distance that brings robustness against outliers …

Abstract

In this paper, we presents a novel method (RGBF-IID) for intrinsic image decomposition of a wild scene without any restrictions on the complexity, illumination or scale of the image. We use focal stacks of the scene as input. A focal stack captures a scene at varying focal distances. Since focus depends on distance to the object, this representation has information beyond an RGB image towards an RGBD image with depth. We call our representation an RGBF image to highlight this. We use a robust focus measure and generalized random walk algorithm to compute dense probability maps across the stack. These maps are used to define sparse local and global pixel neighbourhoods, adhering to the structure of the underlying 3D scene. We use these neighbourhood correspondences with standard chromaticity assumptions as constraints in an optimization system. We present our results on both indoor and outdoor scenes using manually captured stacks of random objects under natural as well as artificial lighting conditions. We also test our system on a larger dataset of synthetically generated focal stacks from NYUv2 and MPI Sintel datasets and show competitive performance against current state-of-the-art IID methods that use RGBD images. Our method provides a strong evidence for the potential of RGBF modality in place of RGBD in computer vision.

Abstract

We address the problem of predicting similarity between a pair of handwritten document images written by potentially 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. Our method does not depend on an accurate segmentation of words and lines. We formulate the document matching problem as a structured comparison of the word distributions across two document images. To match two word images, we propose a convolutional neural network (cnn) based feature descriptor. Performance of this representation surpasses the state-of-the-art on handwritten word spotting. Finally, we demonstrate the applicability of our …

Abstract

n this paper, we introduce the cartoon faces in the wild (IIIT-CFW) database and associated problems. This database contains 8,928 annotated images of cartoon faces of 100 public figures. It will be useful in conducting research on spectrum of problems associated with cartoon understanding. Note that to our knowledge, such realistic and large databases of cartoon faces are not available in the literature.

Abstract

We propose a novel partial linearization based approach for optimizing the multi-class svm learning problem. Our method is an intuitive generalization of the Frank-Wolfe and the exponentiated gradient algorithms. In particular, it allows us to combine several of their desirable qualities into one approach: (i) the use of an expectation oracle (which provides the marginals over each output class) in order to estimate an informative descent direction, similar to exponentiated gradient; (ii) analytical computation of the optimal step-size in the descent direction that guarantees an increase in the dual objective, similar to Frank-Wolfe; and (iii) a block coordinate formulation similar to the one proposed for Frank-Wolfe, which allows us to solve large-scale problems. Using the challenging computer vision problems of action classification, object recognition and gesture recognition, we demonstrate the efficacy of our …

Abstract

We present a dynamic story generation approach for the egocentric videos from the heritage sites. Given a short video clip of a ‘heritage-tour’ our method selects a series of short descriptions from the collection of pre-curated text and create a larger narrative. Unlike in the past, these narratives are not merely monotonic static versions from simple retrievals. We propose a method to generate on the fly dynamic narratives of the tour. The series of the text messages selected are optimised over length, relevance, cohesion and information simultaneously. This results in ‘tour guide’ like narratives which are seasoned and adapted to the participants selection of the tour path. We simultaneously use visual and gps cues for precision localization on the heritage site which is conceptually formulated as a graph. The efficacy of the approach is demonstrated on a heritage site, Golconda Fort, situated in Hyderabad …

Abstract

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.