Research Interests:

Most of my research falls under the umbrella of Machine Learning.

The focus of my current research is on computer-based image segmentation, which is a key step in computer vision. Computer vision is concerned with the theory for building artificial systems that obtain information from images, i.e., machines that can “see”. It is a relatively young field that attempts to emulate the extremely complex human visual system by using image capturing equipment as the "eyes" and computers and algorithms as the "brain". A fundamental task of the human visual system is the ability to find  objects in an image, i.e., segmenting an image into semantically meaningful regions. Thus, over the last four decades, the development of image segmentation algorithms has been an area of considerable research activity.  Many image segmentation algorithms have been elaborated. However, strong segmentation, which corresponds to a partitioning of an image's pixels into regions that are semantically meaningful to people, remains a difficult and as yet largely unsolved problem. Most current image segmentation algorithms extract regions satisfying some uniformity (homogeneity) criterion which is based on low-level data-driven visual features (e.g., color, texture). Those algorithms perform well in narrow domains (e.g., medical images, frontal views of faces) where the variability of low-level visual content is limited. Unfortunately, in broader domains, homogeneous regions do not necessarily (and usually do not) correspond to semantically meaningful objects. This is mainly caused by the disconnection between low-level visual features and high-level semantics, which is commonly referred to as the semantic gap. Thus, the current image-processing-based segmentation paradigm, which relies exclusively on low-level visual content, may have reached its limits. To build strong image segmentation algorithms that break through the performance ceiling imposed by the semantic gap, we must introduce high-level semantic information into the segmentation process. My current research deals with the development of systematic image segmentation strategies which, through the use of machine learning, incorporate higher level knowledge into the segmentation process.

My Ph.D. dissertation investigated the possibility of exploiting long-term learning to improve the performance of content-based image retrieval (CBIR). CBIR is an area of intensive research. It aims at retrieval of images from a database that are relevant to a query image based on automatically derived image features (e.g., color, texture). The relevance of a database image to the query image is proportional to the distance between their corresponding points in the feature space. Unfortunately, human notion of similarity is usually based on high-level abstractions such as activities, events, or emotions displayed in an image. As a result, images with high feature similarity to the query image may be completely different from it in terms of semantics. Thus, the semantic gap is also an open challenging problem in CBIR. Relevance feedback (RF) has been proposed as a learning technique aimed at reducing the semantic gap. It works by gathering semantic information from user interaction. Based on the user's feedback on the retrieval results, the retrieval scheme is adjusted. A disadvantage of traditional RF approaches is that the captured knowledge in one query session is not memorized and the learning starts from ground up for each new query. While short-term learning has been widely used in the literature, less research has been focused on exploiting long-term learning.

The focus of my master thesis was on the development of a parallel genetic algorithm for the graph theory/combinatorial problem of finding Ramsey Numbers.

Please look at my list of publications to see what I am currently thinking about.

 
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