17,567 research outputs found
Plant image retrieval using color, shape and texture features
We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques
and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered
Using video objects and relevance feedback in video retrieval
Video retrieval is mostly based on using text from dialogue and this remains the most signi¯cant component, despite progress in other aspects. One problem with this is when a searcher wants to locate video based on what is appearing in the video rather than what is being spoken about. Alternatives such as automatically-detected features and image-based keyframe matching can be used, though these still need further improvement in quality. One other modality for video retrieval is based on segmenting objects from video and allowing end users to use these as part of querying. This uses similarity between query objects and objects from video, and in theory allows retrieval based on what is actually appearing on-screen. The main hurdles to greater use of this are the overhead of object segmentation on large amounts of video and the issue of whether we can actually achieve effective object-based retrieval.
We describe a system to support object-based video retrieval where a user selects example video objects as part of the query. During a search a user builds up a set of these which are matched against objects previously segmented from a video library. This match is based on MPEG-7 Dominant Colour, Shape Compaction and Texture Browsing descriptors. We use a user-driven semi-automated segmentation process to segment the video archive which is very accurate and is faster than conventional video annotation
A Novel Active Contour Model for Texture Segmentation
Texture is intuitively defined as a repeated arrangement of a basic pattern
or object in an image. There is no mathematical definition of a texture though.
The human visual system is able to identify and segment different textures in a
given image. Automating this task for a computer is far from trivial. There are
three major components of any texture segmentation algorithm: (a) The features
used to represent a texture, (b) the metric induced on this representation
space and (c) the clustering algorithm that runs over these features in order
to segment a given image into different textures. In this paper, we propose an
active contour based novel unsupervised algorithm for texture segmentation. We
use intensity covariance matrices of regions as the defining feature of
textures and find regions that have the most inter-region dissimilar covariance
matrices using active contours. Since covariance matrices are symmetric
positive definite, we use geodesic distance defined on the manifold of
symmetric positive definite matrices PD(n) as a measure of dissimlarity between
such matrices. We demonstrate performance of our algorithm on both artificial
and real texture images
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Perceptual-based textures for scene labeling: a bottom-up and a top-down approach
Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label
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