One of the basic components of image information mining (IIM) systems is feature extraction. Feature extraction delivers a low level “building block” decomposition of the input data. In principle, feature extraction results may depend on the characteristics of the images to be analyzed. In order to avoid a critical dependence on a specific concept, we advocate a general feature finder toolbox 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...

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 ...

Most image segmentation algorithms optimize some mathematical similarity criterion derived from several low-level image features. One possible way of combining different types of features, e.g. colorand texture features on different scales and/or different orientations, is to simply stack all the individual measurements into one high-dimensional feature vector. Due to the nature of such stacked...

Understanding of the scene content of a video sequence is very important for content-based indexing and retrieval of multimedia databases. Research in this area in the past several years has focused on the use of speech recognition and image analysis techniques. As a complimentary effort to the prior work, we have focused on using the associated audio information (mainly the nonspeech portion) ...

This paper describes our participations of LIG and LIRIS to the TRECVID 2008 High Level Features detection task. We evaluated several fusion strategies and especially rank fusion. Results show that including as many low-level and intermediate features as possible is the best strategy, that SIFT features are very important, that the way in which the fusion from the various low-level and intermed...

As part of the Interspeech 2016 COMPARE challenge, the two different sub-challenges Deception and Sincerity are addressed. The former refers to the identification of deceptive speech whereas the degree of perceived sincerity of speakers has to be estimated in the latter. In this paper, we investigate the potential of automatic phone recognition-based features for these use case scenarios. The s...

Semantic feature extraction of video shots and fast video sequence matching are important and required for efficient retrieval in a large video database. In this paper, a novel mechanism of similarity retrieval is proposed. Similarity measure between video sequences considering the spatio-temporal variation through consecutive frames is presented. For bridging the semantic gap between low-level...

A new approach, based on control charts, is presented for the task of recognition of events and scenarios in video image sequences. For each image in the sequence, low level image processing and feature extraction steps result in feature descriptors for objects of interest detected in the images. Control charts analysis is then explored to classify the nature of the activity depicted by the tem...