Abstract

We present an approach for automatically identifying the script of the text localized in the scene images. Our approach is inspired by the advancements in mid-level features. We represent the text images using mid-level features which are pooled from densely computed local features. Once text images are represented using the proposed mid-level feature representation, we use an off-the-shelf classifier to identify the script of the text image. Our approach is efficient and requires very less labeled data. We evaluate the performance of our method on a recently introduced CVSI dataset, demonstrating that the proposed approach can correctly identify script of 96.70% of the text images. In addition, we also introduce and benchmark a more challenging Indian Language Scene Text (ILST) dataset for evaluating the performance of our method.

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

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

Multilingual language processing tasks like statistical machine translation and cross language information retrieval rely mainly on availability of accurate parallel corpora. Manual construction of such corpus can be extremely expensive and time consuming. In this paper we present a simple yet efficient method to generate huge amount of reasonably accurate parallel corpus with minimal user efforts. We utilize the availability of large number of English books and their corresponding translations in other languages to build parallel corpus. Optical Character Recognizing systems are used to digitize such books. We propose a robust dictionary based parallel corpus generation system for alignment of multilingual text at different levels of granularity (sentence, paragraphs, etc). We show the performance of our proposed method on a manually aligned dataset of 300 Hindi-English sentences and 100 English-Malayalam sentences.

Abstract

Multilingual language processing tasks like statistical machine translation and cross language information retrieval rely mainly on availability of accurate parallel corpora. Manual construction of such corpus can be extremely expensive and time consuming. In this paper we present a simple yet efficient method to generate huge amount of reasonably accurate parallel corpus with minimal user efforts. We utilize the availability of large number of English books and their corresponding translations in other languages to build parallel corpus. Optical Character Recognizing systems are used to digitize such books. We propose a robust dictionary based parallel corpus generation system for alignment of multilingual text at different levels of granularity (sentence, paragraphs, etc). We show the performance of our proposed method on a manually aligned dataset of 300 Hindi-English sentences and 100 English-Malayalam sentences.

Abstract

Content based indexing is critical to the effective access of the multimedia data. To this end, visual data is often annotated with textual content for bridging the semantic gap. In this paper, we present a method to generate frame level fine grained annotations for a given video clip. Access to the frame level fine grained annotations lead to rich, dense and meaningful semantic associations between the text and video. This in turn makes the video retrieval systems more accurate. We demonstrate the use of probabilistic label consistent sparse coding and dictionary learning with a K-SVD algorithm to generate `fine grained' annotations for a class of videos - lawn tennis. The algorithm simultaneously learns a classifier and a dictionary to generate the frame level annotations for the tennis videos using available textual descriptions. The utility of the proposed algorithm is demonstrated on a publicly available tennis dataset …

Abstract

An examination of object recognition challenge leaderboards (ILSVRC, PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small differences amongst themselves in terms of error rate/mAP. To better differentiate the top performers, additional criteria are required. Moreover, the (test) images, on which the performance scores are based, predominantly contain fully visible objects. Therefore, ‘harder’ test images, mimicking the challenging conditions (e.g. occlusion) in which humans routinely recognize objects, need to be utilized for benchmarking. To address the concerns mentioned above, we make two contributions. First, we systematically vary the level of local object-part content, global detail and spatial context in images from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12. Second, we propose an object-part based benchmarking procedure which quantifies classifiers’ robustness to a range of visibility and contextual settings. The benchmarking procedure relies on a semantic similarity measure that naturally addresses potential semantic granularity differences between the category labels in training and test datasets, thus eliminating manual mapping. We use our procedure on the PPSS-12 dataset to benchmark top-performing classifiers trained on the ILSVRC-2012 dataset. Our results show that the proposed benchmarking procedure enables additional differentiation among state-of-the-art object classifiers in terms of their ability to handle missing content and insufficient object detail. Given this capability for additional differentiation, our approach can potentially supplement existing benchmarking procedures used in object recognition challenge leaderboards.

Abstract

Vertical video editing is the process of digitally editing the image within the frame as opposed to horizontal video editing, which arranges the shots along a timeline. Vertical editing can be a time-consuming and error-prone process when using manual key-framing and simple interpolation. In this paper, we present a general framework for automatically computing a variety of cinematically plausible shots from a single input video suitable to the special case of live performances. Drawing on working practices in traditional cinematography, the system acts as a virtual camera assistant to the film editor, who can call novel shots in the edit room with a combination of high-level instructions and manually selected keyframes.