Abstract

Many documents are created by Cut-And-Paste(CAP) of existing documents. In this paper, we proposed a noveltechnique to detect CAP in document images. This can helpin detecting unethical CAP in document image collections. Oursolution is recognition free, and scalable to large collection ofdocuments. Our formulation is also independent of the imagingprocess (camera based or scanner based) and does not use anylanguage specific information for matching across documents. Wemodel the solution as finding a mixture of homographies, anddesign a linear programming (LP) based solution to compute thesame. Our method is presently limited by the fact that we do notsupport detection of CAP in documents formed by editing of thetextual content.Our experiments demonstrate that without loss of generality(i.e. without assuming the number of source documents), we cancorrectly detect and match the CAP content in a questioneddocument image by simultaneously comparing with large numberof images in the database. We achieve the CAP detection accuracyof as high as 90%, even when the spatial extent of the CAP contentin a document image is as small as 15% of the entire image area.

Abstract

Parsing for clothes in images and videos is a critical step towards understanding the human appearance. In this work, we propose a method to segment clothes in settings where there is no restriction on number and type of clothes, pose of the person, viewing angle, occlusion and number of people. This is a challenging task as clothes, even of the same category, have large variations in color and texture. The presence of human joints is the best indicator for cloth types as most of the clothes are consistently worn around the joints. We incorporate the human joint prior by estimating the body joint distributions using the detectors and learning the cloth-joint co-occurrences of different cloth types with respect to body joints. The cloth-joint and cloth-cloth co-occurrences are used as a part of the conditional random field framework to segment the image into different clothing. Our results indicate that we have outperformed the recent attempt [16]on H3D [3], a fairly complex dataset.

Abstract

We address the problem of automatic image annotation in large vocabulary datasets.In such datasets, for a given label, there could be several other labels that act as its confusing labels. Three possible factors for this are (i) incomplete-labeling (“cars”vs.“vehicle”), (ii) label-ambiguity (“flowers”vs.“blooms”), and (iii) structural-overlap(“lion”vs.“tiger”). While previous studies in this domain have mostly focused on nearest-neighbour based models, we show that even the conventional one-vs-rest SVM significantly outperforms several benchmark models. We also demonstrate that with a simple modification in the hinge-loss of SVM, it is possible to significantly improve its performance. In particular, we introduce a tolerance-parameter in the hinge-loss. Thismakes the new model more tolerant against the errors in the classification of samples tagged with confusing labels as compared to other samples. This tolerance parameter is automatically determined using visual similarity and dataset statistics. Experimental evaluations demonstrate that our method (referred to as SVM with Variable Tolerance or SVM-VT) shows promising results on the task of image annotation on three challenging datasets, and establishes a baseline for such models in this domain.

Abstract

Understanding positional semantics of the environment plays an important role in manipulating an object in clutter. The interaction with surrounding objects in the environment must be considered in order to perform the task without causing the objects fall or get damaged. In this paper, we learn the semantics in terms of support relationship among different objects in a cluttered environment by utilizing various photometric and geometric properties of the scene. To manipulate an object of interest, we use the inferred support relationship to derive a sequence in which its surrounding objects should be removed while causing minimal damage to the environment. We believe, this work can push the boundary of robotic applications in grasping, object manipulation and picking-from-bin, towards objects of generic shape and size and scenarios with physical contact and overlap. We have created an RGBD dataset that consists of various objects used in day-to-day life present in clutter. We explore many different settings involving different kind of object-object interaction. We successfully learn support relationships and predict support order in these settings.

Abstract

Detecting compact overlapping communities in large networks is an important pattern recognition proble mwith applications in many domains. Most community de-tection algorithms trade-off between community sizes, their compactness and the scalability of finding communities. Clique Percolation Method (CPM) [1] and Local Fitness Maximization(LFM) [2] are two prominent and commonly used overlapping community detection methods that scale with large networks.However, significant number of communities found by the mare large, noisy, and loose. In this paper, we propose a general algorithm that takes such large and loose communities generated by any method and refines them into compact com-munities in a systematic fashion. We define a new measure of community-ness based on eigenvector centrality, identify loose communities using this measure and propose an algorithm for partitioning such loose communities into compact communities.We refine the communities found by CPM and LFM using our method and show their effectiveness compared to the original communities in a recommendation engine task.

Abstract

Large unstructured photo collections from Inter-net usually have distinguishable keyword tagging associated with the images. Photos from tourist and heritage sites can be described with detailed and part-wise annotations resulting in an improved automatic search and enhanced photo browsing experience. Manually annotating a large community photo collection is a costly and redundant process as similar images share the same annotations. We demonstrate an interactive web-based annotation tool that allows multiple users to add,view, edit and suggest rich annotations for images in com-munity photo collections. Since, distinct annotations could befew, we have an easy and efficient batch annotation approach using an image similarity graph, pre-computed with instance retrieval and matching. This helps in seamlessly propagating annotations of the same objects or similar images across the entire dataset. We use a database of20Kimages (Heritage-20K) taken from a world-famous heritage site to demonstrate and evaluate our annotation approach.

Abstract

Labels associated with social images are valuable source of information for tasks of image annotation, understanding and retrieval.These labels are often found to be noisy, mainly due to the collabo-rative tagging activities of users. Existing methods on annotation havebeen developed and verified on noise free labels of images. In this paper,we propose a novel and generic framework that exploits the collective knowledge embedded in noisy label co-occurrence pairs to derive robust annotations. We compare our method with a well-known image annota-tion algorithm and show its superiority in terms of annotation accuracy on benchmark Corel5K and ESP datasets in presence of noisy labels.

Abstract

In this work, we aim at understanding semantic interaction among graspable objects in both direct and indirect physical contact for robotic manipulation tasks. Given an object of interest, its support relationship with other graspable objects is inferred hierarchically. The support relationship is used to predict the “support order” or the order in which the surrounding objects need to be removed in order to manipulate the target object. We believe, this can extend the scope of robotic manipulation tasks to typical clutter involving physical contact, overlap and objects of generic shapes and sizes. We have created an RGBD dataset consisting of various objects present in clutter using Kinect. We conducted our experimentation and analysed the performance of our work on the images from the same dataset.

Abstract

Traditional clustering algorithms use a predefined metric and no supervision in identifying the partition. Existing semi-supervised clustering approaches either learn a metric from randomly chosen constraints or actively select informative constraints using a generic distance measure like Euclidean norm. We tackle the problem of identifying constraints that are informative to learn appropriate metric for semi-supervised clustering. We propose an approach to simultaneously find out appropriate constraints and learn a metric to boost the clustering performance. We evaluate clustering quality of our approach using the learned metric on the MNIST handwritten digits, Caltech-256 and MSRC2 object image datasets. Our results on these datasets have significant improvements over the baseline methods like MPCK-MEANS.

Abstract

Labels associated with social images are valuable source of information for tasks of image annotation, understanding and retrieval. These labels are often found to be noisy, mainly due to the collaborative tagging activities of users. Existing methods on annotation have been developed and verified on noise free labels of images. In this paper, we propose a novel and generic framework that exploits the collective knowledge embedded in noisy label co-occurrence pairs to derive robust annotations. We compare our method with a well-known image annotation algorithm and show its superiority in terms of annotation accuracy on benchmark Corel5K and ESP datasets in presence of noisy labels.