The goal of person text-image matching is to retrieve images specific pedestrians using natural language. Although a lot research results have been achieved in persona matching, existing methods still face two challenges. First,due the ambiguous semantic information features, aligning textual features with their corresponding image always tricky. Second, absence each local feature poses signifi...

In recent years, the abundance of information in 3D data has made semantic segmentation point clouds a topic great interest. However, current methods often rely solely on original three-dimensional coordinates cloud as input geometric features, leading to poor generalization performance. Additionally, occlusion can negatively impact accuracy when only local is considered. To address these issue...

Robust semantic labeling of image regions is a basic problem in representing and retrieving image/video content. We propose an SVM-MRF framework to model features and their spatial distributions, leading towards a “semantic” representation. Eigenfeatures of Gabor wavelet features and Gaussian mixture model are used for feature clustering. Since similar feature vectors in one cluster can come fr...

Structured link vector model (SLVM) and its improved version depend on statistical term measures to implement XML document representation. As a result, they ignore the lexical semantics of terms and its mutual information, leading to text classification errors. This paper proposed a XML document representation method, WordNet-based lexical-semantic SLVM, to solve the problem. Using WordNet, thi...

This paper proposes a classification network to image semantic retrieval (NIST) framework to counter the image retrieval challenge. Our approach leverages the successful classification network GoogleNet based on Convolutional Neural Networks to obtain the semantic feature matrix which contains the serial number of classes and corresponding probabilities. Compared with traditional image retrieva...