Non-uniform bird assemblages in urban environments : the influence of streetscape vegetation

The urban landscape encompasses a broad spectrum of variable environments ranging from remnant patches to highly modified streetscapes. Despite the expansion of urban environments, few studies have examined the influence of urbanization on faunal diversity, particularly in the Southern Hemisphere. In this study, four broad habitat types were recognized in the urban environment, representing a continuum of modification ranging from parks with remnant vegetation to streetscapes dominated by native vegetation and those dominated by exotic vegetation to recently developed streetscapes. Bird censuses were conducted at 36 sites throughout urban Melbourne, with nine sites surveyed in each habitat type. The four habitat types supported significantly different bird communities based on species richness, abundance and composition suggesting that bird assemblages of urban environments are non-uniform. Parks and native streetscapes generally supported fewer introduced species than exotic and recently developed streetscapes. Overall abundance and richness of species were lower in the exotic and recently developed streetscapes than in parks and native streetscapes. Significant differences were also observed in foraging guilds within the four habitat types, with parks having the most foraging guilds and recently developed streetscapes having the fewest. The transition from native to exotic streetscapes saw the progressive loss of insectivorous and nectarivorous species reflecting a reliance by these species on structurally diverse and/or native vegetation for both shelter and food resources. The implementation of effective strategies and incentives which encourage the planting of structurally diverse native vegetation in streetscapes and gardens should be paramount if avian biodiversity is to be retained and enhanced in urban environments. It is also critical to encourage the maintenance of the existing remnant vegetation in the urban environment.

Using object and trajectory analysis to facilitate indexing and retrieval of video

This paper aims to show that by using low level feature extraction, motion and object identifying and tracking methods, features can be extracted and indexed for efficient and effective retrieval for video; such as an awards ceremony video. Video scene/shot analysis and key frame extraction are used as a foundation to identify objects in video and be able to find spatial relationships within the video. The compounding of low level features such as colour, texture and abstract object identification lead into higher level real object identification and tracking and scene detection. The main focus is on using a video style that is different to the heavily used sports and news genres. Using different video styles can open the door to creating methods that could encompass all video types instead of specialized methods for each specific style of video.

Integrating local one-class classifiers for image retrieval

In content-based image retrieval, learning from users’ feedback can be considered as an one-class classification problem. However, the OCIB method proposed in [1] suffers from the problem that it is only a one-mode method which cannot deal with multiple interest regions. In addition, it requires a pre-specified radius which is usually unavailable in real world applications. This paper overcomes these two problems by introducing ensemble learning into the OCIB method: by Bagging, we can construct a group of one-class classifiers which emphasize various parts of the data set; this is followed by a rank aggregating with which results from different parameter settings are incorporated into a single final ranking list. The experimental results show that the proposed I-OCIB method outperforms the OCIB for image retrieval applications.

Coherence based histograms for shape retrieval

Histograms have been used for Shape Representation and Retrieval. The drawback of the histograms method is that histograms can be same for dissimilar shapes, which renders the method less effective for retrieval of shapes. In this paper, we describe the concept of coherence. We show how coherence can be used with distance and angular histograms. We perform experiments to test the effectiveness of the proposed method. It is found that coherence improves accuracy of retrieval significantly.

Angular histograms for shape retrieval

Distance histograms have been used for Shape Representation and Retrieval [1][2]. In this paper, we have proposed the use of angular histograms for shape representation. We have implemented a system for conducting experiments and evaluating the effectiveness of the proposed method. The proposed method is compared with the distance histograms method. It is found that the
proposed method is effective.

Image retrieval using modified generic fourier descriptors

Generic Fourier Descriptors have been used for image retrieval [12]. In this paper, we have proposed a modification to the Generic Fourier Descriptors. We have performed experiments to compare the performance of the proposed method with the standard method. Tests were performed on Set B of the MPEG-7 Still Images Content Set [13]. The experimental results show the effectiveness of the proposed method.

Multi-scale analysis of connectivity for image retrieval

Previously, we proposed the concept of connectivity to obtain discriminating shape descriptors. In this paper, we use connectivity to obtain superior distance histograms for multi-scale images. Experiments are performed to evaluate the distance histograms, based on connectivity, for shape-based retrieval of multi-scale images. Item S8 within the MPEG-7 still images content set is used for performing experiments. Experimental results show that the proposed method enhances retrieval performance significantly.

Spherical harmonics descriptor for 2D-image retrieval

In this paper, spherical harmonics are proposed as shape descriptors for 2D images. We introduce the concept of connectivity; 2D images are decomposed using connectivity, which is followed by 3D model construction. Spherical harmonics are obtained for 3D models and used as descriptors for the underlying 2D shapes. Difference between two images is computed as the Euclidean distance between their spherical harmonics descriptors. Experiments are performed to test the effectiveness of spherical harmonics for retrieval of 2D images. Item S8 within the MPEG-7 still images content set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the proposed descriptors for 2D images are effective

A composite descriptor for shape retrieval

In this paper, a composite descriptor for shape retrieval has been proposed. The proposed descriptor is obtained from Generic Fourier Descriptors (GFD) for the shape region and the shape contour. A composite descriptor derived from GFD of the shape region and the shape contour is used for indexing and retrieval of shapes. Difference between two images is computed as the Euclidean distance between their composite descriptors. Experiments are performed to test the effectiveness of the proposed descriptor for retrieval of 2d images. Sets of composite descriptors, obtained by assigning different weights to the region component and the contour component, are also evaluated. Item S8 within the MPEG-7 Still Images Content Set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the proposed descriptor is effective.

An image retrieval system based on fractal dimension

This paper presents a new kind of image retrieval system which obtains the feature vectors of images by estimating their fractal dimension; and at the same time establishes a tree-structure image database. After preprocessing and feature extracting, a given image is matched with the standard images in the image database using a hierarchical method of image indexing.