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.

Abstract

Commercial applications like driver assistance programs in cars, smile detection softwares in cameras typically require reliable facial landmark points like the location of eyes, lips etc. and face pose at near real-time. Current methods are often unreliable, very cumbersome or computationally intensive. In this work, we focus on implementing a reliable and real-time method which parses an image and detects faces, estimates their pose and locates landmark points on the face. Our method builds on the existing literature. The method can work both for images and videos.

Abstract

Sparse representation based image restoration tech-niques have shown to be successful in solving various inverse problems such as denoising, in painting, and super-resolution,etc. on natural images and videos. In this paper, we explore the use of sparse representation based methods specifically to restore the degraded document images. While natural images form a very small subset of all possible images admitting the possibility of sparse representation, document images are significantly more restricted and are expected to be ideally suited for such a representation. However, the binary nature of textual document images makes dictionary learning and coding techniques unsuitable to be applied directly. We leverage the fact that different characters possess similar strokes, curves, and edges, and learn a dictionary that gives sparse decomposition for patches. Experimental results show significant improvement in image quality and OCR performance on documents collected from a variety of sources such as magazines and books. This method is therefore, ideally suited for restoring highly degraded images in repositories such as digital libraries.

Abstract

Sparse representations have emerged as a powerful approach for encoding images in a large class of machine recognition problems including face recognition. These methods rely on the use of an over-complete basis set for representing an image. This often assumes the availability of a large number of labeled training images, especially for high dimensional data. In many practical problems, the number of labeled training samples are very limited leading to significant degradations in classification performance. To address the problem of lack of training samples, we propose a semi-supervised algorithm that labels the unlabeled samples through a multi-stage label propagation combined with sparse representation. In this representation, each image is decomposed as a linear combination of its nearest basis images, which has the advantage of both locality and sparsity.Extensive experiments on publicly available face databases show that the results are significantly better compared to state-of-the-art face recognition methods in semi-supervised setting and are on par with fully supervised techniques