A classic approach toward zero-shot learning (ZSL) is to map the input domain to a set of semantically meaningful attributes that could be used later on to classify unseen classes of data (e.g. visual data). In this paper, we propose to learn a visual feature dictionary that has semantically meaningful atoms. Such a dictionary is learned via joint dictionary learning for the visual domain and the attribute domain, while enforcing the same sparse coding for both dictionaries. Our novel attribute aware formulation provides an algorithmic solution to the domain shift/hubness problem in ZSL. Upon learning the joint dictionaries, images from unseen classes can be mapped into the attribute space by finding the attribute aware joint sparse representation using solely the visual data. We demonstrate that our approach provides superior or comparable performance to that of the state of the art on benchmark datasets.

We address zero-shot learning using a new manifold alignment framework based on a localized multi-scale transform on graphs. Our inference approach includes a smoothness criterion for a function mapping nodes on a graph (visual representation) onto a linear space (semantic representation), which we optimize using multi-scale graph wavelets. The robustness of the ensuing scheme allows us to operate with automatically generated semantic annotations, resulting in an algorithm that is entirely free of manual supervision, and yet improves the state-of-the-art as measured on benchmark datasets.

The Contextual Visual Dataspace (CVD) is a real-time representation of an automotive environment that combines automated 3D modeling and semantic labeling of a scene with dynamic object detection using infrastructure cameras. Our automotive active safety concept uses CVD to detect and track dynamic objects of interest, geo-register them into the semantically labeled 3D world space, analyze the paths of vehicles and pedestrians, infer intent and therefore make more accurate predictions of potential collisions, and finally give alerts to drivers and pedestrians and provide them with real-time situational awareness. The CVD system was demonstrated in a blind curve collision warning scenario.

We have developed a new optimization-based framework for computer vision that combines ideas from particle swarm optimization (PSO) and statistical pattern recognition to rapidly and accurately detect and classify objects in visual imagery. Swarm intelligence is used to locate objects by optimizing the classification confidence level. We have used our cognitive swarm framework to rapidly detect people, ground vehicles, and boats, and to recognize behaviors based on object associations, such as people exiting and entering vehicles, for applications in security, surveillance, target recognition, and automotive active safety.

Object fingerprinting and identification is a critical part of effective visual surveillance systems. In this paper, we present an approach to actively learn the object models in order to fingerprint the objects. Our approach uses a view-based classifier cascade that actively learns to recognize the generic class of the object. Salient features unique to the specific instance of the selected class of objects are modeled using fuzzy attribute relational graphs. These graphs are also adapted to represent object information gathered from multiple views. Preliminary results are quite promising and extensive studies are underway to ascertain the use of the system in more complicated scenarios.

The paper proposes a new set of fuzzy features based on symmetry of edges for improving the accuracy of detecting intruders. We show that the proposed fuzzy edge-symmetry feature-based classifier is comparable to the detection accuracy of a multi-scale wavelet feature system for intruder detection. We also present two approaches to fusing the results of classifiers trained independently on the edge-symmetry and wavelet features. Experimental results clearly indicate the improvement in system performance when the results of the two classifiers are fused.

In this paper we present an approach that is capable of online learning and automatic generation of a fuzzy expert system for high dimensional classification problems. The novel part of our system is a new online learning rule. Unlike other learning systems, this learning rule makes our system scale robustly with input space dimensions and thus suitable for high dimensional data. The algorithm is also able to extract knowledge in an online fashion in the form of fuzzy rules that are comprehensible, compact, and accurate.