Due to the short duration and low intensity of micro-expressions, the recognition of micro-expression is still a challenging problem. In this paper, we develop a novel multi-task mid-level feature learning method to enhance the discrimination ability of extracted low-level features by learning a set of class-specific feature mappings, which would be used for generating our mid-level feature rep...

Low-level features tend to achieve unsatisfactory retrieval results in remote sensing image retrieval community because of the existence of semantic gap. In order to improve retrieval precision, visual attention model is used to extract salient objects from image according to their saliency. Then color and texture features are extracted from salient objects and regarded as feature vectors for i...

Road, railway and track networks are important features of satellite imagery. Automated detection of tracks in mountainous terrain using satellite imagery is a difficult task. The track and the shadow signature of the mountainous terrain are almost same. The presence of ridges and snow also creates a lot of ambiguity. Moreover due to poor sunlight condition in the mountainous terrain the images...

Bilkent University Multimedia Database Group (BILMDG) participated in two tasks at TRECVID 2008: content-based copy detection (CBCD) and high-level feature extraction (FE). Mostly MPEG-7 [1] visual features, which are also used as low-level features in our MPEG-7 compliant video database management system, are extracted for these tasks. This paper discusses our approaches in each task.

This paper aims to show that by using low level feature extraction, motion and object identifying and tracking methods, features can be extracted and indexed for eYcient and eVective retrieval for video; such as an awards ceremony video. Video scene/shot analysis and key frame extraction are used as a foundation to identify objects in video and be able to Wnd spatial relationships within the vi...

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...

In this paper a study of some structural features of handwritten letter ‘a’ is presented. The features under consideration are structural, style and formation features document examiner’s use when studying a questioned document to determine its authorship or authenticity. Feature extraction algorithms for the feature set were developed to perform the extraction procedure automatically. The esti...

Recently much attention has been paid to Image ContentBased Retrieval Systems (CBRS). One important goal in CBRS is to extract local low level image features such as color, texture and shape, to allow queries based on these features. A large CBRS containing tens of thousands of images requires an automatic feature-extraction method since human aided segmentation is impractical. We address this ...

We investigate how to represent a natural image in order to be able to recognize the visual concepts within it. The core of the proposed method consists in a new approach to aggregate local features, based on a non-parametric estimation of the Fisher vector, that result from the derivation of the gradient of the loglikelihood. For this, we need to use low level local descriptors that are learne...

In this paper, we summarize our results for the shot boundary and high level feature detection task at TRECVID 2007. Our shot boundary detection approach of previous TRECVID evaluations served as a basis for our experiments this year and was modified in several ways. First, we have incorporated a new metric selection for cut detection based on the evaluation of a clustering result. Second, we h...