This paper reports our experiments on the concept detection task of TRECVID 2007. In these experiments, we have addressed two approaches which are selecting and fusing features and kernel-based learning method. As for the former one, we investigate the following issues: (i) which features are more appropriate for the concept detection task?, (ii) whether the fusion of features can help to impro...

Histopathological image classification is one of the most important steps for disease diagnosis. We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network. It can gain better representation for the histopathological image than only using coding network. The main process is that training a deep convolutiona...

Most of the existing image retrieval systems take the textual query from the user and utilize the metadata associated with the database images to retrieve the result. However, the results of these systems are substandard. This paper presents a framework for the matching and retrieval of archaeological images based on highly specific visual features. As a result, the potential users of the syste...

High Level Feature Extraction runs. 1. A VITALAS.CERTH.ITI 1: Combination of early fusion and concept score fusion with feature selection. 2. A VITALAS.CERTH.ITI 2: Concept score fusion with feature selection. 3. A VITALAS.CERTH.ITI 3: Clustering within feature space and concept score fusion with feature selection. 4. A VITALAS.CERTH.ITI 4: Concept score fusion for selected low level features. ...

In this paper, based on the Mobile E-commerce Platform, we implement the Information Retrieval System of image-based high-level semantic by using the feature extraction algorithm based on object semantic. By way of optimizing the image feature extraction algorithm, improving the structure of the traditional Search Engine and increasing the carrying capacity of mobile terminals, we will solve a ...

Unsupervised feature learning has emerged as a promising tool in learning representations from unlabeled data. However, it is still challenging to learn useful high-level features when the data contains a significant amount of irrelevant patterns. Although feature selection can be used for such complex data, it may fail when we have to build a learning system from scratch (i.e., starting from t...

We present a method to learn maximal generalized decision rules from databases by integrating discretization, generalization and rough set feature selection. Our method reduces the data horizontally and vertically. In the first phase, discretization and generalization are integrated and the numeric attributes are discretized into a few intervals. The primitive values of symbolic attributes are ...