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A correlation-enhanced similarity matching framework for medical image retrieval is presented in a local concept-based feature space. In this framework, images are presented by vectors of concepts that comprise of local color and texture patches of image regions in a multi-dimensional feature space. To generate the concept vocabularies and represent the images, statistical models are built using a...
Age estimation from facial images has promising applications in human-computer interaction, biometrics, visual surveillance, and electronic customer relationship management, etc. Most existing techniques and systems can only handle frontal or near frontal view age estimation due to the difficulties of 1) differentiating diverse variations from uncontrollable and personalized aging patterns on faces...
This paper proposes a method of feature co-occurrence representation based on boosting for object detection. A previously proposed method that combines multiple binary-classified codes by AdaBoost to represent the co-occurrence of features has been shown to be effective in face detection. However, if an input feature is difficult to be assigned to a correct binary code due to occlusion or other factors,...
In this work we tackle the problem of search personalization for on-line soft goods shopping. By learning what the user likes and what the user does not like, better search rankings and therefore a better overall shopping experience can be obtained. The first contribution of the work is in terms of feature selection: given the specific nature of the domain, we combine the traditional visual and text...
In this paper we study some problems important for large-scale human age estimation. First, we study age estimation performance under variations across race and gender. Through a large number of age estimation experiments, significant differences are observed for age estimation between “no crossing” and “crossing.” Our study discovers that crossing race and gender can result in significant error increases...
Tracking and detection of objects often require to apply complex models to cope with the large intra-class variability of the foreground as well as the background class. In this work, we reduce the complexity of a binary classification problem by a context-driven approach. The main idea is to use a hidden multi-class representation to capture multi-modalities in the data finally providing a binary...
In this paper, the principles of sparse signal representation theory are explored in order to perform facial expressions recognition from frontal views. Motivated by the success such methods have demonstrated in the face recognition problem, we formulate the feature extraction procedure in order to achieve facial expression recognition as an l1 optimization problem. We show that the straightforward...
In this paper, we present a new perceptual grouping algorithm using sparse semi-supervised learning (SSSL). In SSSL, KD-tree is used for effective representation and efficient retrieval. SSSL performs both transductive and inductive inference with a new dynamic graph concept. The perceptual grouping problem is tackled using SSSL to group different patterns into one object and separate similar patterns...
In this paper, we examine the problem of learning a representation of image transformations specific to a complex object class, such as faces. Learning such a representation for a specific object class would allow us to perform improved, pose-invariant visual verification, such as unconstrained face verification. We build off of the method of using factored higher-order Boltzmann machines to model...
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