Multi-class Video Objects Segmentation Based on Conditional Random Fields
نویسندگان
چکیده
Video object segmentation has been widely used in many fields. A conditional random fields (CRF) model is proposed to achieve accurate multi-class segmentation of video objects in the complex environment. By using CRF, the color, texture, motion characteristics and neighborhood relations of objects are modeled to construct the corresponding energy functions in both the temporal and spatial domains. The model is trained with annotated samples by using LogitBoost classifier. The energy function is amended by adding a constraint factor which is used to indicate the interaction between two adjacent images in the video sequence. Experimental results show that the proposed algorithm can achieve high performance for multi-class objects segmentation in videos under complex environment. It can also get good recognition results when dealing with multi-viewed objects or serious sheltered objects. Copyright © 2014 IFSA Publishing, S. L.
منابع مشابه
Broadcast News Story Segmentation Using Conditional Random Fields and Multimodal Features
This paper proposes to integrate multi-modal features using conditional random fields (CRF) for broadcast news story segmentation. We study story boundary cues from lexical, audio and video modalities, where lexical features consist of lexical similarity, chain strength and overall cohesiveness, acoustic features involve pause duration, pitch, speaker change and audio event type, and visual fea...
متن کاملTextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation
This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned model is used for automatic visual recognition and semantic segmentation of photographs. Our discriminative model exploits novel features, based on textons, which jointly model shape and texture. Unary classification and featur...
متن کاملClassification of high resolution remote sensing image based on Geo- ontology and Conditional Random Fields
The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represent...
متن کاملProbabilistic Multi-class Scene Flow Segmentation for Traffic Scenes
A multi-class traffic scene segmentation approach based on scene flow data is presented. Opposed to many other approaches using color or texture features, our approach is purely based on dense depth and 3D motion information. Using prior knowledge on tracked objects in the scene and the pixel-wise uncertainties of the scene flow data, each pixel is assigned to either a particular moving object ...
متن کاملChinese Word Segmentation and Named Entity Recognition Based on Conditional Random Fields
Chinese word segmentation (CWS), named entity recognition (NER) and part-ofspeech tagging is the lexical processing in Chinese language. This paper describes the work on these tasks done by France Telecom Team (Beijing) at the fourth International Chinese Language Processing Bakeoff. In particular, we employ Conditional Random Fields with different features for these tasks. In order to improve ...
متن کامل