Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval
The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents. CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors include texture, color, intensity and shape of the object inside an image. Several feature-extraction techniques viz., Average RGB, Color Moments, Co-occurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper. However, individually these techniques result in poor performance. So, combinations of these techniques have also been evaluated and results for the most efficient combination of techniques have been presented and optimized for each class of image query. We also propose an improvement in image retrieval performance by introducing the idea of Query modification through image cropping. It enables the user to identify a region of interest and modify the initial query to refine and personalize the image retrieval results.
Code (0)
등록된 구현이 없습니다.
Tasks
Content-Based Image RetrievalImage CroppingImage RetrievalRetrievalSimilar Papers 제목 키워드 기반
Comparative analysis of common edge detection techniques in context of object extraction
Edges characterize boundaries and are therefore a problem of practical importance in remote sensing.In this paper a comparative study of various edge detection techniques and band wise analysis of these algorithms in the…
Edge DetectionOn the Use of Different Feature Extraction Methods for Linear and Non Linear kernels
The speech feature extraction has been a key focus in robust speech recognition research; it significantly affects the recognition performance. In this paper, we first study a set of different features extraction methods…
Robust Speech RecognitionSpeaker Identificationspeech-recognitionSpeech RecognitionAnomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics
This paper showcases an experimental study on anomaly detection using computer vision. The study focuses on class distinction and performance evaluation, combining OpenCV with deep learning techniques while employing a T…
Anomaly DetectionComputational EfficiencyData AugmentationDeep Learning+5Comparative study of Discrete Wavelet Transforms and Wavelet Tensor Train decomposition to feature extraction of FTIR data of medicinal plants
Fourier-transform infra-red (FTIR) spectra of samples from 7 plant species were used to explore the influence of preprocessing and feature extraction on efficiency of machine learning algorithms. Wavelet Tensor Train (WT…
ClusteringGeneral ClassificationComparative Study of Domain Driven Terms Extraction Using Large Language Models
Keywords play a crucial role in bridging the gap between human understanding and machine processing of textual data. They are essential to data enrichment because they form the basis for detailed annotations that provide…
Document SummarizationHallucinationInformation RetrievalKeyword Extraction+2