1:00, IMDSP-L8.1
CONTENT-BASED RETRIEVAL OF VIDEO SHOT USING THE IMPROVED NEAREST FEATURE LINE METHOD
L. ZHAO, W. QI, S. LI, S. YANG, H. ZHANG
The shot based classification and retrieval is very important for video database organization and access. In this paper we present a new approach ¡®Nearest Feature Line ¨C NFL¡¯ used in shot retrieval. We look key-frames in shot as feature points to represent the shot in feature space. Lines connecting the feature points are further used to approximate the variations in the whole shot. The similarity between the query image and the shots in video database are measured by calculating the distance between the query image and the feature lines in feature space. To make it more suitable to video data, we improved the original NFL method by adding constrains on the feature lines. Experimental results show that our improved NFL method is better than the traditional classification methods such as Nearest Neighbor (NN) and Nearest Center (NC).

1:20, IMDSP-L8.2
DISTANCE-FROM-BOUNDARY AS A METRIC FOR TEXTURE IMAGE RETRIEVAL
G. GUO, H. ZHANG, S. LI
A new metric is proposed for texture image retrieval, which is based on the signed distance of the images in the database to a boundary chosen by the query. This novel metric has three advantages: 1) the boundary distance measures are relatively insensitive to the sample distributions; 2) same retrieval results can be obtained with respect to different (but visually similar) queries; 3) retrieval performance can be improved. The boundaries are obtained by using a statistical learning algorithm called support vector machine (SVM), and hence the boundaries can be simply represented by some vectors and their combination coefficients. Experimental results on the Brodatz texture database indicate that a significantly better retrieval performance can be achieved as compared to the traditional Euclidean distance based approach. This technique can be further developed to learn pattern similarities among different texture classes and used in relevance feedback.

1:40, IMDSP-L8.3
FISCHLAR: AN ON-LINE SYSTEM FOR INDEXING AND BROWSING BROADCAST TELEVISION CONTENT
N. O'CONNOR, S. MARLOW, N. MURPHY, A. SMEATON, P. BROWNE, S. DEASY, H. LEE, K. MCDONALD
This paper describes a demonstration system which automatically indexes broadcast television content for subsequent non-linear browsing. User-specified television programmes are captured in MPEG-1 format and analysed using a number of video indexing tools such as shot boundary detection, keyframe extraction, shot clustering and news story segmentation. A number of different interfaces have been developed which allow a user to browse the visual index created by these analysis tools. These interfaces are designed to facilitate users locating video content of particular interest. Once such content is located, the MPEG-1 bitstream can be streamed to the user in real-time. This paper describes both the high-level functionality of the system and the low-level indexing tools employed, as well as giving an overview of the different browsing mechanisms employed (see http://lorca.compapp.dcu.ie/Video).

2:00, IMDSP-L8.4
IMPROVING IMAGE RETRIEVAL PERFORMANCE WITH NEGATIVE RELEVANCE FEEDBACK
T. ASHWIN, N. JAIN, S. GHOSAL
Learning user perception of an image is a challenging issue in interactive content-based image retrieval (CBIR) systems. These systems employ relevance feedback mechanism to learn user perception in terms of a set of model-parameters and in turn iteratively improve the retrieval performance. Since the quantity of user feedback is expected to be small, learning the user's perception essentially involves parameter estimation with very few training points. We propose a novel, and more efficient method for relevance feedback in this paper. Contrary to existing geometric model-based relevance feedback methods, the proposed technique explicitly uses information about irrelevant data points to estimate the parameters of the model. This algorithm iteratively updates the parameters of the similarity metric so as to fit the relevant examples while excluding the irrelevant ones. This is achieved by modifying the weights associated with the relevant examples. Experiments on image and synthetic datasets demonstrate the retrieval effectiveness of the proposed approach.

2:20, IMDSP-L8.5
INTERACTIVE CBIR USING RBF-BASED RELEVANCE FEEDBACK FOR WT/VQ CODED IMAGES
P. MUNEESAWANG, L. GUAN
Powerful interfaces provide great potential for retrieval systems to adapt to dynamic user needs and allow a more accurate modeling of image similarity from the users’ point of view. In this paper, we propose a novel method within the interactive framework. It allows the users to directly modify the system characteristics by specifying their desired image attributes in the form of training samples. More specifically, we have adopted a radial basis function (RBF) method for implementing an adaptive metric which progressively models the notion of image similarity through continual feedback from the users. The proposed approach has been integrated into an image retrieval system using images compressed by wavelet transform and vector quantization coders. Comparisons with some of the recent systems using the standard texture database indicate that the proposed method provides the more favorable retrieval result.

2:40, IMDSP-L8.6
SCENE CUT DETECTION FROM MPEG VIDEO STREAMS CODED WITHOUT B PICTURES
A. DAWOOD, M. GHANBARI
In this paper we propose an algorithm that automatically detects clear scene cut locations from an MPEG-1 video bit streams coded with a GOP structure of M=1; without B pictures. The algorithm detects scene cuts at P type pictures by monitoring the percentage of Intra-macroblocks per P picture. while scene cuts at I pictures are detected by matching the macroblocks type of the two P pictures at the GOP boundaries. A ``Type Matching Parameter'' (TMP) is developed to estimate the matching degree between the macroblock types of two P pictures. It is shown that the method is able to identify the location of scene cuts in P and I pictures with a high success rate.