A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

Knowledge assisted data management and retrieval in multimedia database systems

With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular, the focus of this study is to facilitate the high-level video indexing by proposing a multimodal event mining framework associated with temporal knowledge discovery approaches. With respect to the perception subjectivity issue, advanced techniques are proposed to support users' interaction and to effectively model users' perception from the feedback at both the image-level and object-level.

Metodo de sincronização de cameras de video utilizando a banda de audio

Universidade Estadual de Campinas . Faculdade de Educação Física

Metodologia para descrição tridimensional do movimento humano a partir de cameras de video não sincronizadas

Universidade Estadual de Campinas. Faculdade de Educação Física

LEARNING A COMPLEX MOTOR SKILL FROM VIDEO AND POINT-LIGHT DEMONSTRATIONS

The aim of this Study was to compare the learning process of a highly complex ballet skill following demonstrations of point light and video models 16 participants divided into point light and video groups (ns = 8) performed 160 trials of a pirouette equally distributed in blocks of 20 trials alternating periods of demonstration and practice with a retention test a day later Measures of head and trunk oscillation coordination d1 parity from the model and movement time difference showed similarities between video and point light groups ballet experts evaluations indicated superiority of performance in the video over the point light group Results are discussed in terms of the task requirements of dissociation between head and trunk rotations focusing on the hypothesis of sufficiency and higher relevance of information contained in biological motion models applied to learning of complex motor skills

A New Approach for Video Adaptation through Overlay Services Networks

Video adaptation is an extensively explored content providing technique aimed at appropriately suiting several usage scenarios featured by different network requirements and constraints, user`s terminal and preferences. However, its usage in high-demand video distribution systems, such as CNDs, has been badly approached, ignoring several aspects of optimization of network use. To address such deficiencies, this paper presents an approach for implementing the adaptation service by exploring the concept of overlay services networks. As a result of demonstrate the benefits of this proposal, it is made a comparison of this proposed adaptation service with other strategies of video adaptation.

A synchronous multimedia annotation system for secure collaboratories

In this paper, we describe the Vannotea system - an application designed to enable collaborating groups to discuss and annotate collections of high quality images, video, audio or 3D objects. The system has been designed specifically to capture and share scholarly discourse and annotations about multimedia research data by teams of trusted colleagues within a research or academic environment. As such, it provides: authenticated access to a web browser search interface for discovering and retrieving media objects; a media replay window that can incorporate a variety of embedded plug-ins to render different scientific media formats; an annotation authoring, editing, searching and browsing tool; and session logging and replay capabilities. Annotations are personal remarks, interpretations, questions or references that can be attached to whole files, segments or regions. Vannotea enables annotations to be attached either synchronously (using jabber message passing and audio/video conferencing) or asynchronously and stand-alone. The annotations are stored on an Annotea server, extended for multimedia content. Their access, retrieval and re-use is controlled via Shibboleth identity management and XACML access policies.

Is YouTube telling or selling you something? Tobacco content on the YouTube video sharing website

The effectiveness of overt tobacco advertising and sponsorship bans is well established. The industry has responded to these bans by implementing “buzz” or “viral” marketing techniques, such as nightclub and dance party promotions. This paper analyses possible tobacco industry content on the burgeoning consumer generated media website, YouTube. Tobacco control efforts need to embrace this new medium in order to counter pro-smoking messages and maximize media advocacy opportunities.

Inter-subband redundancy prediction using neural network for video coding

High performance video codec is mandatory for multimedia applications such as video-on-demand and video conferencing. Recent research has proposed numerous video coding techniques to meet the requirement in bandwidth, delay, loss and Quality-of-Service (QoS). In this paper, we present our investigations on inter-subband self-similarity within the wavelet-decomposed video frames using neural networks, and study the performance of applying the spatial network model to all video frames over time. The goal of our proposed method is to restore the highest perceptual quality for video transmitted over a highly congested network. Our contributions in this paper are: (1) A new coding model with neural network based, inter-subband redundancy (ISR) prediction for video coding using wavelet (2) The performance of 1D and 2D ISR prediction, including multiple levels of wavelet decompositions. Our result shows a short-term quality enhancement may be obtained using both 1D and 2D ISR prediction.

A New Family of Distance Functions for Perceptual Similarity Retrieval of Medical Images

A long-standing challenge of content-based image retrieval (CBIR) systems is the definition of a suitable distance function to measure the similarity between images in an application context which complies with the human perception of similarity. In this paper, we present a new family of distance functions, called attribute concurrence influence distances (AID), which serve to retrieve images by similarity. These distances address an important aspect of the psychophysical notion of similarity in comparisons of images: the effect of concurrent variations in the values of different image attributes. The AID functions allow for comparisons of feature vectors by choosing one of two parameterized expressions: one targeting weak attribute concurrence influence and the other for strong concurrence influence. This paper presents the mathematical definition and implementation of the AID family for a two-dimensional feature space and its extension to any dimension. The composition of the AID family with L (p) distance family is considered to propose a procedure to determine the best distance for a specific application. Experimental results involving several sets of medical images demonstrate that, taking as reference the perception of the specialist in the field (radiologist), the AID functions perform better than the general distance functions commonly used in CBIR.

Conditioning training and retrieval increase phospholipase A(2) activity in the cerebral cortex of rats

In rats, phospholipase A(2) (PLA(2)) activity was found to be increased in the hippocampus immediately after training and retrieval of a contextual fear conditioning paradigm (step-down inhibitory avoidance [IA] task). In the present study we investigated whether PLA(2) is also activated in the cerebral cortex of rats in association with contextual fear learning and retrieval. We observed that IA training induces a rapid (immediately after training) and long-lasting (3 h after training) activation of PLA(2) in both frontal and parietal cortices. However, immediately after retrieval (measured 24 h after training), PLA(2) activity was increased just in the parietal cortex. These findings suggest that PLA(2) activity is differentially required in the frontal and parietal cortices for the mechanisms of contextual learning and retrieval. Because reduced brain PLA(2) activity has been reported in Alzheimer disease, our results suggest that stimulation of PLA(2) activity may offer new treatment strategies for this disease.

Laparoscopic resection of uncinate process of the pancreas

Solid pseudopapillary neoplasm of the pancreas is an uncommon but distinctive pancreatic neoplasm with low metastatic potential [1]. Therefore, whenever feasible, an organ-preserving operation should be performed. As previously reported, women with solid pseudopapillary neoplasm of the pancreas may be best treated by more conservative procedures [2]. Recently, laparoscopic pancreatic resections became more common and are being performed in highly specialized centers. There are only six cases of laparoscopic resection for solid pseudopapillary neoplasm of pancreas published in the English literature and, to our knowledge, laparoscopic resection of uncinate process of the pancreas has never been reported [3-6]. This video demonstrates the technical aspects of a totally laparoscopic resection of the uncinate process of the pancreas in a patient with solid pseudopapillary neoplasm. A 26-year-old woman with a 4-cm solid pseudopapillary pancreatic neoplasm was referred for surgical treatment. According to preoperative echoendoscopy, there was a safe margin between neoplasm and main pancreatic duct. The patient was placed in supine position with the surgeon standing between her legs. Four trocars, one 10-mm and three 5-mm, were used. At inspection, the inferior vena cava, transverse colon, duodenum, and pancreas are clearly identified. A Kocher maneuver was performed with complete exposure of pancreatic head and uncinate process. The uncinate process was dissected from the superior mesenteric vein and venous branches were divided between metallic clips or by use of laparoscopic coagulation shears (LCS; Ethicon Endo Surgery Industries, Cincinnati, OH, USA). Blood supply of the duodenum was preserved by ligature of small pancreatic branches from inferior pancreatoduodenal artery. Transection of pancreatic parenchyma was performed using laparoscopic coagulation shears, which is an effective tool for cutting the pancreas [7, 8]. Surgical specimen was removed through a suprapubic incision inside a retrieval bag. A hemostatic absorbable tissue (Surgicel; Ethicon Inc., Cincinnati, OH) was placed in the cutting pancreatic surface, and one round 19F Blake abdominal drain (Ethicon) was left in place. Operative time was 180 minutes and blood loss estimated in 40 ml with no blood transfusion. Hospital stay was 4 days. The patient did not have postoperative pancreatitis or pancreatic leakage, and the abdominal drain was removed on the tenth postoperative day. Final pathology confirmed the diagnosis of solid pseudopapillary neoplasm of pancreas with free surgical margins. The patient was well and asymptomatic 2 months after the procedure. Laparoscopic resection of uncinate process of the pancreas is safe and feasible and should be considered for patients suffering from pancreatic neoplasms.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

Comparative analysis of two immunohistochemical methods for antigen retrieval in the optical lobe of the honeybee Apis mellifera: Myosin-v assay

The present study compared two heating methods currently used for antigen retrieval (AR) immunostaining: the microwave oven and the steam cooker. Myosin-V, a molecular motor involved in vesicle transport, was used as a neuronal marker in honeybee Apis mellifera brains fixed in formalin. Overall, the steam cooker showed the most satisfactory AR results. At 100 degrees C, tissue morphology was maintained and revealed epitope recovery, while evaporation of the AR solution was markedly reduced; this is important for stabilizing the sodium citrate molarity of the AR buffer and reducing background effects. Standardization of heat-mediated AR of formalin-fixed and paraffin-embedded tissue sections results in more reliable immunostaining of the honeybee brain.