Shape-based Image Retrieval

Shape-based Image Retrieval By Aditya Vailaya Retrieval efficiency and accuracy are two important issues in designing a contentbased database retrieval system. We propose a new image database retrieval method based on shape information. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable statistical shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals; in the second stage, the outputs from the first stage are screened using a deformable template matching process to discard spurious matches. We have tested the algorithm using hand drawn queries on a trademark database containing 1; 100 images. Each retrieval takes a reasonable amount of computation time (¸ 45 seconds). The top most retrieved image by the system agrees with that obtained by human subjects, but there are significant differences between the top 10 retrieved images by our ..

Shape-Based Retrieval: A Case Study with Trademark Image Databases

Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval system. We propose a method for trademark image database retrieval based on object shape information that would supplement traditional text-based retrieval systems. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals when a query is presented; in the second stage, the candidates from the first stage are screened using a deformable template matching process to discard spurious matches. We have tested the algorithm using hand drawn queries on a trademark database containing 1; 100 images. Each retrieval takes a reasonable amount of computation time (¸ 4-5 seconds on a Sun Sparc 20 workstation). The top most image retrieved by the system agrees with that obtained by human subjects, ..

A Hierarchical System for Efficient Image Retrieval

Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval system. We propose a new image database retrieval method based on shape information. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable statistical shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals; in the second stage, the outputs from the first stage are screened using a deformable template matching process to discard spurious matches. We have tested the algorithm using hand drawn queries on a trademark database containing 1; 100 images. Each retrieval takes a reasonable amount of computation time. The top most retrieved image from the system agrees with that obtained by human subjects, but there are significant differences between the top 10 retrieved images by our system and that provided by human subjects. This demonstra..

On Image Classification: City Images vs. Landscapes

Grouping images into semantically meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, effective indices can be built for an image database. In this paper, we show how a specific high-level classification problem (city images vs. landscapes) can be solved from relatively simple low-level features geared for the particular classes. We have developed a procedure to qualitatively measure the saliency of a feature towards a classification problem based on the plot of the intra-class and inter-class distance distributions. We use this approach to determine the discriminative power of the following features: color histogram, color coherence vector, DCT coefficient, edge direction histogram, and edge direction coherence vector. We determine that the edge direction-based features have the most discriminative power for the classification problem of interest here. A weighted k-NN classifier is use..

A Bayesian framework for semantic classification of outdoor vacation images

Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, effective indices can be built for an image database. In this paper, we cast the image classification problem in a Bayesian framework. Specifically, we consider city vs. landscape classification and further classification of landscape images into sunset, forest, and mountain classes. We demonstrate how high-level concepts can be understood from specific low-level image features under the constraint that the test images do belong to one of the classes in concern. We further demonstrate that a small codebook (the optimal size of codebook is selected using MDL principle) extracted from a vector quantizer can be used to estimate the class-conditional densities needed for the Bayesian methodology. Classification based on color histograms, color coherence vectors, edge direction histograms, and edge direction coherence vectors as features shows promising results. On a database of city and landscape images, our system achieved an accuracy of for city vs. landscape classification. On a subset of landscape images, our system achieves an accuracy of for sunset vs. forest & mountain classification and for forest vs. mountain classification. Our final goal is to combine multiple-class classifiers into a single hierarchical classifier.

Video Clustering

We address the issue of clustering of video images. We assume that video clips have been segmented into shots which are further represented by a set of keyframes. Video clustering is thus reduced to a clustering of still keyframe images. Experiments with 8 human subjects reveal that humans tend to use semantic meanings while grouping a set of images. A complete-link dendrogram constructed from the similarities provided by the subjects revealed two significant categories of images; that of city scenes and landscapes. A hierarchical clustering based on moments of 17 DCT coefficients of the JPEG compressed keyframe images reveals that ad hoc low-level features are not capable of identifying semantically meaningful categories in an image database. It is well known that a clustering scheme will always find clusters in a data set! In order to define categories that will aid in indexing and browsing of video data, features specific to a given semantic class should be used. As an example, we ..