Olcay Guldogan

MUVIS: a content-based multimedia indexing and retrieval framework

MUVIS is a series of CBIR systems. The first one has been developed in late 90s to support indexing and retrieval in large image databases using visual and semantic features such as color, texture and shape. During recent years, MUVIS has been reformed to become a PC-based framework, which supports indexing, browsing and querying of various multimedia types such as audio, video, audio/video interlaced and several image formats. MUVIS system allows real-time audio and video capturing, encoding by last generation codecs such as MPEG-4, H.263+, MP3 and AAC. It supports several audio/video file format such as AVI, MP4, MP3 and AAC. Furthermore, MUVIS system provides a well-defined interface for third parties to integrate their own feature extraction algorithms into the framework and for this reason it has recently been adopted by COST 211quat as COST framework for CBIR. In this paper, we describe the general system features with underlying applications and outline the main philosophy.

Compression effects on color and texture based multimedia indexing and retrieval

This paper presents an evaluation of digital compression effects on content-based multimedia retrieval using color and texture attributes. Subjective evaluation tests that are applied on digital image and video databases using different compression and visual feature extraction techniques have been performed and reported. Simulations show that a satisfactory retrieval performance can be obtained from the compressed databases with 10% compression quality (i.e. 97.6% compression ratio in JPEG). Image retrieval based on HSV color histogram performs better than retrieval based on YUV color histogram in the uncompressed domain, and the other way around in the compressed domain. In general, video retrieval based on color histogram in MPEG-4 compressed databases performs better compared to H.263+ compressed databases. However, retrieval performance from H.263+ compressed databases at lower bit rates is more stable, where it drastically decreases in MPEG-4 compressed databases below 128 Kb/s. Retrieval based on texture features produces more robust performance than retrieval based on color. Subjective tests show that 25% compression quality achieves high compression ratio without loosing significant retrieval performance. The results are particularly relevant to applications in which a mobile device is involved in a multimedia retrieval system.

Tool-supported customization of UML class diagrams for learning complex system models

To employ an existing software library, its structure should be first learned and the required elements should be identified. This can be challenging if the library is large and only a specific part of it should be comprehended. In this paper, we study the problem of learning complex software libraries modeled in UML. It is argued that the learning process can be supported with a tool environment that allows the customization of the UML model according to the context of the learner, stepwise and dynamically chosen learning tasks, and focusing on a particular learning concern at a time. We show how such an environment can be achieved based on the concept of a pattern, using existing tool support. We demonstrate the idea with a part of Symbian platform architecture. The approach is evaluated in a case study where a pattern-driven learning environment is constructed for JPEG interchange file format specifications.

Visual media retrieval using transform-based layered query scheme

This paper presents a visual media querying scheme referred to as transform-based layered query (TLQ) scheme. The TLQ scheme mainly aims at decreasing retrieval processing time and run-time memory consumption without degrading retrieval results semantically. The scheme contains abstract layers in indexing and retrieval phases, where each indexing layer corresponds to a retrieval layer. The layers are constructed based on transformations for reducing visual frame and feature data dimensions. The proposed TLQ scheme also involves an unsupervised method for eliminating irrelevant media items between the retrieval layers. A two-layer TLQ system is implemented and integrated into MUVIS content-based multimedia indexing and retrieval framework, and its theoretical advantages are verified with dedicated experiments on image and video databases. The experiments reveal that 75% retrieval performance improvement in terms of process time can be achieved depending on transformation parameters.

Automated tree detection and density calculation using unmanned aerial vehicles

Growth monitoring in tree plantation farms plays a crucial role in taking necessary actions from early stages to improve harvesting efficiency. Unmanned aerial vehicles (UAVs) provide a fast and efficient way to acquire data from large farms with difficult access. We propose a novel end-to-end system composed of multiple stages to identify areas where seeds have not grown as expected in young tree farms. The system acquires data from UAV flights to generate georeferenced orthophoto images. Next, local binary patterns, distance transform, and watershed segmentation methods are applied on images to detect trees. Finally, tree density distributions are calculated in the granularity of approximately 90 m2 tiles. We assessed the system performance by comparing detection results against a ground truth set of over 50000 trees from 16 orthophoto images for two tree species. The median deviation was 2 trees per tile for both species, where the median tree count was 14 and 9 per tile, respectively.