Automatic stereoscopic video object-based watermarking using qualified significant wavelet trees

In this paper a fully automatic scheme for embedding visually recognizable watermark patterns to video objects is proposed. The architecture consists of 3 main modules. During the first module unsupervised video object extraction is performed, by analyzing stereoscopic pairs of frames. In the second module each video object is decomposed into three levels with ten subbands, using the Shape Adaptive Discrete Wavelet Transform (SA-DWT) and three pairs of subbands are formed (HL3 , HL2), (LH3, LH2) and (HH3, HH2). Next Qualified Significant Wavelet Trees (QSWTs) are estimated for the specific pair of subbands with the highest energy content. QSWTs are derived from the Embedded Zerotree Wavelet (EZW) algorithm and they are high-energy paths of wavelet coefficients. Finally during the third module, visually recognizable watermark patterns are redundantly embedded to the coefficients of the highest energy QSWTs and the inverse SA-DWT is applied to provide the watermarked video object. Performance of the proposed video object watermarking system is tested under various signal distortions such as JPEG lossy compression, sharpening, blurring and adding different types of noise. Furthermore the case of transmission losses for the watermarked video objects is also investigated. Experimental results on real life video objects indicate the efficiency and robustness of the proposed schemeFacultad de Informátic

Implicit visual concept modeling in image / video annotation

In this paper a novel approach for automatically annotating image databases is proposed. Despite most current approaches that are just based on spatial content analysis, the proposed method properly combines implicit feedback information and visual concept models for semantically annotating images. Our method can be easily adopted by any multimedia search engine, providing an intelligent way to even annotate completely non-annotated content. The proposed approach currently provides very interesting results in limited-content environments and it is expected to add significant value to billions of non-annotated images existing in the Web. Furthermore expert annotators can gain important knowledge relevant to user new trends, language idioms and styles of searchin

Adaptive Classification-based Articulation and Tracking of Video Objects Employing Neural Network Retraining

In this paper, an adaptive neural network architecture is proposed for efficient video object segmentation and tracking of stereoscopic video sequences. The scheme includes (a) a retraining algorithm for adapting network weights to current conditions, (b) a semantically meaningful object extraction module for creating a retraining set and (c) a decision mechanism, which detects the time instances of a new network retraining. The retraining algorithm optimally adapts network weights by exploiting information of the current conditions and simultaneously minimally degrading the obtained network knowledge. The algorithm results in the minimization of a convex function subject to linear constraints, thus, one minimum exists. Furthermore, a decision mechanism is included to detect the time instances that a new network retraining is required. Description of the current conditions is provided by a segmentation fusion algorithm, which appropriately combines color and depth information

Human action analysis, annotation and modeling in video streams based on implicit user interaction

This paper proposes an integrated framework for analyzing human actions in video streams. Despite most current approaches that are just based on automatic spatiotemporal analysis of sequences, the proposed method introduces the implicit user-in-the-loop concept for dynamically mining semantics and annotating video streams. This work sets a new and ambitious goal: to recognize, model and properly use "average user's" selections, preferences and perception, for dynamically extracting content semantics. The proposed approach is expected to add significant value to hundreds of billions of non-annotated or inadequately annotated video streams existing in the Web, file servers, databases etc. Furthermore expert annotators can gain important knowledge relevant to user preferences, selections, styles of searching and perceptio

Unsupervised clustering of clickthrough data for automatic annotation of multimedia content

Current low-level feature-based CBIR methods do not provide meaningful results on non-annotated content. On the other hand manual annotation is both time/money consuming and user-dependent. To address these problems in this paper we present an automatic annotation approach by clustering, in an unsupervised way, clickthrough data of search engines. In particular the query-log and the log of links the users clicked on are analyzed in order to extract and assign keywords to selected content. Content annotation is also accelerated by a carousel-like methodology. The proposed approach is feasible even for large sets of queries and features and theoretical results are verified in a controlled experiment, which shows that the method can effectively annotate multimedia file

Tube-Embodied Gradient Vector Flow Fields for Unsupervised Video Object PLANE (VOP) SEGMENTATION

In this paper constrained Gradient Vector Flow (GVF) field generation is performed, for fast and accurate unsupervised stereoscopic semantic segmentation. The scheme utilizes the information provided by a depth segments map, produced by stereo analysis methods and incorporation of a segmentation algorithm. Then a Canny edge detector is applied to the depth region and produces an edge map. The edge map is used for tube estimation inside which the GVF field evolves. After generation of the GVF field an active contour is unsupervisedly initialized onto the outer bound of the tube. Finally a greedy approach is adopted and the active contour, guided by the GVF field, extracts the VOP. Experimental results on real life stereoscopic video sequences indicate the efficiency of the proposed scheme