Efficient CBIRS Using fused feature ELM

computer science and engineering

Abstract: Content based image retrieval, in the last few years has received a wide attention. Content Based Image Retrieval (CBIR) basically is a technique to perform retrieval of images from a large database which are similar to image given as query. CBIR is closer to human semantics, in the context of image retrieval process. In a typical CBIR system, image low level features like color, texture, shape and spatial locations are represented in the form of a multidimensional feature vector. The feature vectors of images in the database form a feature database. The retrieval process is initiated when a user query the system using an example image or sketch of the object. The query image is converted into the internal representation of feature vector using the same feature extraction routine that was used for building the feature database. The proposed framework initially selects pertinent images from a large databases using color moment information. Color information can be extracted from the image by both global and local techniques. consequently local binary pattern(LBP) and edges detection methods are used to extract the texture and edge features respectively from the query and resultant images of initial stage of this framework .The texture information is often estimated locally from the gray-level representation of an image and ELM for machine learning classifier is designed for unfussiness and speed.

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