Face recognition is an interesting and a challenging problem that has been widely studied in the field of pattern recognition and computer vision. It has many applications such as biometric authentication, video surveillance, and others. In the past decade, several methods for face recognition were proposed. However, these methods suffer from pose and illumination variations. In order to address these problems, this paper proposes a novel methodology to recognize the face images. Since image gradients are invariant to illumination and pose variations, the proposed approach uses gradient orientation to handle these effects. The Schur decomposition is used for matrix decomposition and then Schurvalues and Schurvectors are extracted for subspace projection. We call this subspace projection of face features as Schurfaces, which is numerically stable and have the ability of handling defective matrices. The Hausdorff distance is used with the nearest neighbor classifier to measure the similarity between different faces. Experiments are conducted with Yale face database and ORL face database. The results show that the proposed approach is highly discriminant and achieves a promising accuracy for face recognition than the state-of-the-art approaches.
This article considers a system for the semiautomatic annotation of an audio-visual media of dance domain, DMAR (Dance Media Annotation, authoring and Retrieval system). We present the architecture of the system, a manual annotation tool, a semiautomatic authoring tool and a search engine for the choreographers, dancers and students to demonstrate how the dance media can be semantically annotated and how this information can be used for the retrieval of the dance media objects. This article then outlines the underlying XML Schema based content description structures of DMAR and discusses the merits and demerits of our approach of evolving semantic network as the basis for the audio-visual content description. Further, this article proposes a quality metric, fidelity to evaluate the expressive power of the dance annotations. Finally, evaluation results are presented to depict the performance of the dance video queries in terms of precision and recall.
Abstract. An approach for extracting higher-level visual features for art painting classification based on MPEG-7 descriptors is presented in this paper. The MPEG-7 descriptors give a good presentation of different types of visual features, but are complex structures. This prevents their direct use into standard classification algorithms and thus requires specific processing. Our approach consists of the following steps: (1) the images are tiled into non-overlapping rectangles to capture more detailed information; (2) the tiles of the images are clustered for each MPEG-7 descriptor; (3) vector quantization is used to assign a unique value to each tile, which corresponds to the number of the cluster where the tile belongs to, in order to reduce the dimensionality of the data. Finally, the significance of the attributes and the importance of the underlying MPEG-7 descriptors for class prediction in this domain are analyzed.
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