Survey on Mining Semantically Consistent Patterns for Cross-View Data

We often face the situation that the similar information is represented by different views with different backgrounds, in some real world applications such as Information Retrieval and Data classification. So it becomes necessary for those applications to obtain a certain Semantically Consistent Patterns (SCP) for cross-view data, which embeds the complementary information from different views. However, eliminating heterogeneity among cross-view representations is a significant challenge in mining the SCP. This paper reviews the research work on a general framework to discover the SCP for cross-view data web crawling algorithms used on searching a general framework to discover the SCP for cross-view data. DOI: 10.17762/ijritcc2321-8169.160411

Mining Semantically Consistent Patterns for Cross view data with CCA and CJFL

We often faces the situation that the same semantic concept can be expressed using different views with similar information, in some real world applications such as Information Retrieval and Data classification. So it becomes necessary for those applications to obtain a certain Semantically Consistent Patterns (SCP) for cross-view data, which embeds the complementary information from different views. However, eliminating heterogeneity among cross-view representationsis a significant challenge in mining the SCP. The existing work has proposed the effective Isomorphic Relevant Redundant Transformation (IRRT) and Correlation-based Joint Feature Learning (CJFL) method for mining SCP from cross-view data representation. Even though existing system uses the IRRT for SCP from low level to mid-level feature extraction. Some redundant data and noise remains in it. To remove redundant information and noise from mid- level feature space to high level feature space, CJFL algorithm is used. We are using Canonical correlation analysis (CCA) method instead of complex IRRT which also lags to remove the noise and redundant information

Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding

Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness

An empirical study of inter-concept similarities in multimedia ontologies

Generic concept detection has been a widely studied topic in recent research on multimedia analysis and retrieval, but the issue of how to exploit the structure of a multimedia ontology as well as different inter-concept relations, has not received similar attention. In this paper, we present results from our empirical analysis of different types of similarity among semantic concepts in two multimedia ontologies, LSCOM-Lite and CDVP-206. The results show promise that the proposed methods may be helpful in providing insight into the existing inter-concept relations within an ontology and selecting the most facilitating set of concepts and hierarchical relations. Such an analysis as this can be utilized in various tasks such as building more reliable concept detectors and designing large-scale ontologies