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

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

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

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

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

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

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

Performance of the recognition free approaches fordocument retrieval, heavily depends on the exact or approximatematching of images (in some feature space) to retrieve documentscontaining the same word. However, the harder problem in infor-mation retrieval is to effectively bring semantics into the retrievalpipeline. This is further challenging when the matching is basedon visual features. In this work, we investigate this problem,and suggest a solution by directly transferring the semanticsfrom the textual domain. Our retrieval framework uses (i) thelanguage resources like WordNet and (ii) an annotated corpus ofdocument images, to retrieve semantically relevant words from alarge word image database. We demonstrate the method on twolanguages — English and Hindi, and quantitatively evaluate theperformance on annotated word image databases of more thana Million images

Abstract

Recognizing text in images taken in the wild is achallenging problem that has received great attention in recentyears. Previous methods addressed this problem by first detectingindividual characters, and then forming them into words. Suchapproaches often suffer from weak character detections, due tolarge intra-class variations, even more so than characters fromscanned documents. We take a different view of the problemand present a holistic word recognition framework. In this,we first represent the scene text image and synthetic imagesgenerated from lexicon words using gradient-based features. Wethen recognize the text in the image by matching the scene andsynthetic image features with our novel weighted Dynamic TimeWarping (wDTW) approach.We perform experimental analysis on challenging publicdatasets, such as Street View Text and ICDAR 2003. Ourproposed method significantly outperforms our earlier work inMishraet al.(CVPR 2012), as well as many other recent works,such as Novikovaet al.(ECCV 2012), Wanget al.(ICPR 2012),Wanget al.(ICCV 2011)

Abstract

Bag of words (BoW) based retrieval is an efficient method to compare the visual similarity between two images.Recognition free methods based on BoW have shown to out-perform OCR based methods. We further improve the perfor-mance by defining a document specific sparse coding scheme for representing visual words (interest points) in document images.Our method is motivated by the successful use of sparsity insignal representation by exploiting the neighbourhood properties.In addition to providing insights into the design of the coding scheme, we also verify the method on two data sets and compare with the recent methods. We have also developed text query based search solution, and we report performance comparable to image based search