Titlebook: Computer Vision -- ACCV 2012; 11th Asian Conferenc Kyoung Mu Lee,Yasuyuki Matsushita,Zhanyi Hu Conference proceedings 2013 Springer-Verlag

hemoglobin

Online Web-Data-Driven Segmentation of Selected Moving Objects in Videosashion from relevant labeled images ranked in a large-scale Web image set. The method for online prior learning has three steps: (1) relevant silhouette images for training are online selected using a user-provided bounding box and an object class annotation; (2) image patches containing the annotat

LURE

Multi-Level Structured Image Coding on High-Dimensional Image Representation performance in various high level visual recognition tasks. Our work is motivated by the need of exploring rich structural information encoded by these image representations. In this paper, we propose a novel Multi-Level Structured Image Coding approach to uncover the structure embedded in represen

口訣法

Incremental Slow Feature Analysis with Indefinite Kernel for Online Temporal Video Segmentationd by its application for unsupervised activity analysis, we develop SFA’s first implementation of online temporal video segmentation to detect episodes of motion changes. We utilize a domain-specific indefinite kernel which takes the data representation into account to introduce robustness. As our k

內(nèi)行

Texture Classification Based on BIMF Monogenic Signalsthod proposed by N.E.Huang. It consists two parts: Empirical Mode Decomposition (EMD) and Hilbert transform. Images are decomposed to several Bidimensional Intrinsic Mode Functions (BIMFs) by BEMD, which present new multi-scale characters and present illumination invariant. And then, for two-dimensi

野蠻

crescendo

Co-regularized PLSA for Multi-view Clusteringe difficulty of a learning problem of interest. To this end, we propose an extended Probabilistic Latent Semantic Analysis (PLSA) model for multi-view clustering, named Co-regularized PLSA (CoPLSA). CoPLSA integrates individual PLSAs in different views by pairwise co-regularization. The central idea

Prosaic

Quadra-Embedding: Binary Code Embedding with Low Quantization Errorale similarity search used in many computer vision applications including image retrieval. Most of prior techniques have centered around optimizing a set of projections for accurate embedding. In spite of active research efforts, existing solutions suffer both from diminishing marginal efficiency as

Meager

Learning a Context Aware Dictionary for Sparse Representationplete dictionary should be learned from data. This is because the quality of a dictionary greatly affects performance in many respects, including computational. While so far the focus has been on learning compact, reconstructive, and discriminative dictionaries, in this work we propose to retain the

Minuet

Robust Multiple-Instance Learning with Superbagsth the classifier. These steps are iteratively performed on the same training data, thus imputing labels by evaluating the classifier on the data it is trained upon. Consequently this alternating optimization is prone to self-amplification and overfitting. To resolve this crucial issue of popular mu

下邊深陷

Conference proceedings 2013ture analysis and recognition; optical flow and tracking; motion, tracking, and computational photography; video analysis and action recognition; shape reconstruction and optimization; shape from X and photometry; applications of computer vision; low-level vision and applications of computer vision.