Mixture of Visual Features For Content-Based Image Retrieval

Aarti Datir, Dipak Patil
unpublished
Image retrieval system is a computer system for browsing, searching and retrieve images from a huge data set of digital images. Content-based image retrieval (CBIR) is, the problem of searching for digital images in large datasets. CBIR retrieves similar images from huge image dataset based on image features. Content-based means that the searching will evaluate the actual contents of the image. The content of image might refer colors, shapes, textures, or any other information that can be
more » ... n that can be derived from the image itself. There are two major approaches to content-based image retrieval using local image descriptors. One of them is descriptor by descriptor matching and the another one is based on comparison of global image representation that describes the set of local descriptors of each image. Image representation is one of the key issues for large-scale CBIR. Using MPEG-7 descriptors and local descriptors more number of features are extracted from given query image and to reduce the feature size principle component analysis(PCA) is used. These features are embedded and aggregated into a compact vector to avoid indexing each feature individually. In the embedding step, each local descriptor is mapped into a high dimensional vector. The aggregation step integrates all the embedded vectors of an image into a single vector which obtains a compact representation for image retrieval. Subsequently, k-means algorithm is used, clusters are formed and images are trained in addition cosine similarity measure is used to increase the retrieval performance. In re-ranking step, Euclidean distance is used to find similarity and provide efficient searching. Index Terms-Conference and technote papers also have a list of key words (index terms) CBIR, MPEG-7 descriptors,
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