Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.

Art and the Body Image: about Self and Stereotypes

Today's man is socially absorbed by problematic body issues and everything that this means and involves. Literature, publicity, science, technology and medicine compound these issues in a form of this theme that has never been seen before. In the artistic framework, body image is constantly suffering modifications. Body image in sculpture unfolds itself, assuming different messages and different forms. The body is a synonym of subject, an infinite metaphorical history of our looks, desires, that leads one to interrogate his/her image and social and sexual relations. These are understood as a manifestation of individual desires freed from a moral and social imposition. It attempts a return to profound human nature before we are turned into a cloning industry. In thisstudy it isimportant for usto understand in which form doessculpture reflect body image as a sociocultural and psychological phenomenon within the coordinates of our time. To understand how they represent and what artists represent in sculpture as a multiple and complex structure of human sexuality. Today, the sculptural body, expanding its representation, no longer as a reproduction of the corporal characteristics, presents the body in what it possesses of most intimate, unique, human and real, that moves, reacts, feels, suffers and pulsates, a mirror of us all.

Malséance : la mort et l'image dans le (méta)théâtre de Samuel Beckett et de Jean Genet

Both ludic and macabre, the theatrical works of Samuel Beckett and Jean Genet are a paradox to behold. Indeed, as this thesis seeks to illustrate, despite their vastly differing aesthetics, at the core of each playwright’s stage productions is a tension between the characters’ yearning for silence and invisibility, and the continual creation of an often humorous, chaotic, exaggerated or theatrical image that depicts this very longing. Seeking an impossible intersection between their image and their death, they are trapped in a double bind that guarantees aesthetic failure. In order to grasp the close, yet delicate, relationship between the image of death and the death of the image, as presented in the plays of Beckett and Genet, we will explore how the characters’ creative processes deflate the very images — both visual and auditory — that they create. More specifically, we will examine how mimesis both liberates and confines the characters; while the symbolic realm provides the only means of self-representation, it is also a source of profound alienation and powerlessness, for it never adequately conveys meaning. Thus, body, gesture, language and voice are each the site of simultaneous and ceaseless reappearance and disappearance, for which death remains the only (aporetic) cure. Struggling against theatrical form, which demands the actors’ and the audience’s physical presence, both playwrights make shrewd use of metatheatre to slowly empty the stage and thereby suggest the impending, yet impossible, erasure of their characters.

Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations

Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations.