A Wavelet-based Image Retrieval System An ECE 278A Project Report by

In this report, we propose a wavelet-based content descriptor with which we implement an image retrieval system. Initially, we propose the wavelet-based weighted standard deviation texture descriptor. We then show how to extend this descriptor to characterize both texture and color in images. Thus, we obtain a compact feature vector that characterizes images in terms of both texture and color. We use this feature vector to implement an image retrieval system, using the weighted L1 distance measure.

SketchIt: Basketball Video Retrieval Using Ball Motion Similarity

Abstract. A prototype basketball video retrieval system is presented in this report. Retrieval is based on the similarity of ball motion in the clip with that in the query. The system uses a query-by-sketch paradigm, where the user provides a sketch of the desired ball trajectory. The video data is pre-processed to make the ball motion invariant to camera translation. The next stage is dimensionality reduction wherein we model the ball motion as a set of parabolic trajectories. An R-tree is used to index these parabolic representations and search for similar trajectories in a low dimension parametric space. The query is processed to obtain its parametric representation, and a nearest neighbor search is performed for similar parabolas. These query results are then post-processed by assigning scores based on various similarity criteria. The system could be extended to other types of videos and moving objects. As a proof of concept, the system was tested for ball trajectories in basketball video.

Modeling Object Classes In Aerial Images Using Hidden Markov Models

A canonical model is proposed for object classes in aerial images. This model is motivated by the observation that geographic regions of interest are characterized by collections of texture motifs corresponding to geographic processes. Furthermore, the spatial arrangement of the motifs is an important discriminating characteristic. In our approach, the states of a Hidden Markov Model (HMM) correspond to the geographic processes and the state transitions correspond to the spatial arrangement of the processes. A onedimensional approach reduces the computational complexity. The model is shown to be effective in characterizing objects of interest in spatial datasets in terms of their underlying texture motifs. The potential of the model for identifying the classes of unlabeled objects is demonstrated

Players and Ball Detection in Soccer Videos Based on Color Segmentation and Shape Analysis

Abstract. This paper proposes a scheme to detect and locate the players and the ball on the grass playfield in soccer videos. We put forward a shape analysisbased approach to identify the players and the ball from the roughly extracted foreground, which is obtained by a trained, color histogram-based playfield detector and connected component analysis. We employ Euclidean distance transform to extract skeletons for every foreground blob, and then perform shape analysis to remove false alarms (non-player and non-ball blobs) and cutoff the artifacts (mostly due to playfield lines) based on skeleton pruning and reverse Euclidean distance transform. Results are given to demonstrate the proposed algorithm works well in soccer video clips

Graph partitioning active contours for knowledge-based geospatial segmentation

Our contribution in this paper is two-fold. First, we extend our previous curve evolution method based on pairwise similarities. This curve evolution equation combines the grouping abilities of active contours and graph partitioning techniques. Connections of our method to spectral graph partitioning are investigated and comparisons are made. Second, in a model-based segmentation scenario, we propose a method to improve segmentation quality by iteratively modifying the model using feedback from segmentation of a labeled training set. Our purpose here is to segment objects in geo-spatial images by integrating domain knowledge with the segmentation method. We achieve our goal by combining a statistical model for the object with a knowledge-guided segmentation method. Experimental results show that this framework is effective for model-based segmentation of complex geo-spatial objects. 1

Issues Concerning Dimensionality and Similarity Search

Effectiveness and efficiency are two important concerns in using multimedia descriptors to search and access database items. Both are affected by the dimensionality of the descriptors. While higher dimensionality generally increases effectiveness, it drastically reduces efficiency of storage and searching. With regard to effectiveness, relevance feedback is known to be a useful tool to squeeze information from a descriptor. However, not much has been done toward enabling relevance feedback computation using high-dimensional descriptors over a large multimedia dataset. In this context, we have developed new methods that enable us to a) reduce the dimensionality of Gabor texture descriptors without losing on effectiveness, and b) perform fast nearest neighbor search based on the information available during each iteration of a relevance feedback step. Experimental results are presented on real datasets